The advent of environmental DNA (eDNA) represents a pivotal advancement in probing mesophotic communities, offering a non-intrusive avenue for studying marine biodiversity at greater depths. Using this approach, we characterized and compared the mesophotic reef fish assemblages of two West Indian Ocean islands, Mayotte and La Réunion, which are characterized by different geo-morphological contexts. The sequences obtained were assigned taxonomically and grouped into molecular operational taxonomic units to perform richness and beta diversity analyses. The functional diversity of the assemblages was assessed using five traits, enabling each sequence to be assigned to a functional entity corresponding to a specific trait combination. On both islands, the fish assemblages exhibited a comparable level of taxonomic and functional richness, consisting mainly of piscivorous and planktivorous fishes. These assemblages were primarily composed of families such as Serranidae, consistent with expectations for the mesophotic zone. However, beta diversity analyses revealed that the two islands exhibited different taxonomic and functional structures. For example, La Réunion was characterized by a greater importance of the Muraenidae, while Mayotte displayed a higher representation of families strongly associated with coral reefs (e.g., Zanclidae or Malacanthidae). These results suggest that depth-related forcing constrains fish assemblages to some extent, but that differences in structure remains determined by other, more local factors, likely linked to the geo-morphological contexts of the islands and their habitats. This study also revealed that eDNA is a promising method for studying difficult-to-observe taxa, such as moray eels or lanternfish, and may also be relevant for monitoring species depth ranges. Overall, results highlighted the “local scale”, “functionally integrative” and “temporally integrative” characteristics of eDNA for studying mesophotic reef fish assemblages. However, this study also highlights the limitations of reference DNA databases, pointing to future prospects for fully exploiting the potential of eDNA approaches in the mesophotic zones of the Indian Ocean.
[This dataset was processed using the GBIF Metabarcoding Data Toolkit.]
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E, Gimenez M, Crochelet E, Paulin-Fayolle A, Campagnari F, Desbonnes O, et al. 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study focused on two islands differing in mesoscale (geographical features) and microscale (habitat) contexts. The island of Mayotte, located to the north of the Mozambique Channel (west of Madagascar), is surrounded by a double barrier reef. The mesophotic zones in this region are predominantly associated with steeply sloping substrates and complex habitats. Mayotte lies at the epicenter of the “Coral Triangle” in the Western Indian Ocean and is influenced by zonal and regional ocean currents that facilitate larval transport and promote intricate inter-reef connectivity [62]. The island’s geological antiquity, coupled with the well-developed nature of its reef structures, emerges as a substantial driving force behind the manifestation of extraordinary biodiversity in the region. La Réunion, also situated in the southwestern Indian Ocean but approximately 800 km east of Madagascar, contrasts sharply with Mayotte in terms of geological history and isolation. Unlike the relatively ancient geological formation of Mayotte, La Réunion is characterized by its recent and dynamic volcanic origins. Its mesophotic zone generally features gentle slopes, lava flows, and mud substrates.","boundingBox":{"minLatitude":-21.20383,"maxLatitude":-12.812283,"minLongitude":44.96425,"maxLongitude":55.84092,"globalCoverage":false}}],"temporalCoverages":[{"@type":"range"}],"keywordCollections":[{"thesaurus":"N/A","keywords":["metabarcoding","envrionnemental DNA","marine fish","DNA","coral reefs","MDT","eDNA"]}],"project":{"title":"MesoMay 3 program","funding":"The MesoMay 3 program was funded by the Office Français de la Biodiversité (OFB) and the Direction de l’Environnement, de l’Aménagement et du Logement (DEAL) de Mayotte. The nautical resources and staff of the Parc Naturel Marin de Mayotte were used and contributed to the success of this program. The MesoRun program was funded by the European Union's Life program, the Office Français de la Biodiversité (OFB) and the Agence Française de Développement (AFD) through the Life4Best program. However, the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","awards":[],"relatedProjects":[]},"samplingDescription":{"studyExtent":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322870#sec002 : A total of ten geographic stations around Mayotte were sampled from November to December 2021 at depths ranging from 68–89 m and eight stations around La Réunion were sampled between September 2020 to January 2021 at depths of 80–107 m","sampling":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322870#sec002 : These stations were selected to ensure representative sampling of the known mesophotic habitats of both islands. Niskin bottles are commonly used for deep-water sampling in open environments with weak surface currents. However, water sample collection in mesophotic reefs posed additional challenges, particularly along steep coral reef slopes with strong currents. To optimize sampling efficiency, we developed two custom 8-liter sampling bottles made of polyvinyl chloride to collect large water volumes. This approach was chosen based on evidence that large-volume eDNA sampling with in situ filtration is more efficient than smaller-capacity samplers (e.g., 2 liters), as demonstrated by Govindarajan et al. [64]. Before each sampling event, the bottles and divers’ wetsuits were sterilised by soaking them overnight in tanks containing 10% bleach, then sealed in sterile bags for transport. Divers transported the two 8-L bottles and sealed both end with female’s capes at the required depth. Once collected, the two bottles were brought to the surface and the samples combined into a clean plastic box, which was immediately sealed with a watertight lid. The lid was manually pierced to allow filtration of 16-L of water through a silicone tube connected to a peristaltic pump on board the boat. The water was filtered using a Sterlitech filter capsule with a pore size of 0.8 µm. After filtration, the water was removed from the capsule, which was then filled with a preservation solution and transported in a cooler for storage at -20°C before DNA extraction in a laboratory specialized in handling rare and degraded DNA. Note that one sample was collected per station.","methodSteps":["Benchtop to desktop process of environmental DNA :\nTo mitigate contamination risks, eDNA processing was performed at NatureMetrics (Guilford, England) in dedicated eDNA laboratories with unidirectional workflows (from least to most concentrated DNAs). Each step (i.e., eDNA extraction, amplification, library preparation and sequencing) was performed in separate rooms decontaminated with CHEMGENE HLD4L wipes (STARLAB). DNA was extracted using a DNeasy Blood and Tissue Kit (Qiagen), adapted for higher lysate volumes from the filter. Extraction blanks were processed alongside each batch to monitor potential contamination in the extraction process. DNA was purified using a DNeasy PowerClean Pro Cleanup Kit (Qiagen) and quantified with a Qubit dsDNA BR Assay Kit on a Qubit 4.0 fluorometer (Thermo Scientific). Amplifications were performed using the MiFish primer set [69], with 12 replicate PCRs, including a negative and a positive PCR controls (a mock community with a known composition), and by following the steps developed in Díaz et al. [70]: (i) positive replicate PCR amplicons were purified and pooled for each extraction and prepared into sequencing libraries using a second PCR following methods outlined in Illumina’s 16S Metagenomic Sequencing Library Preparation protocol; and (ii) the libraries were pooled in equimolar concentrations and sequenced on an Illumina MiSeq with a V3 2 × 300 bp kit with a final concentration of 15 pM with a 20% PhiX control spike. Reads of MiSeq runs were demultiplexed with bcl2fastq (Illumina supports) and processed via a custom NatureMetrics eDNA analysis pipeline. Paired-end FASTQ reads were merged with USEARCH [71] for each sample. Forward and reverse primers were trimmed from the merged sequences using cutadapt [72] and a length filter applied as appropriate for the assay (140–200 bp) to exclude overly long and overly short amplicons from downstream analysis. These sequences were quality filtered with USEARCH and dereplicated per sample, retaining singletons to obtain zero-radius OTUs (hereafter ZOTUs) as part of the denoising step. Unique sequences from all samples were denoised in a single analysis with UNOISE [73]. Number of sequences that mapped to ZOTUs was calculated and hence a ZOTU-by-sample table was generated with their relative reads proportion in each sample. Finally, low abundance detections were omitted, with filter thresholds set at 0.025% of the read depth of each sample.","Taxonomic assignment and functional database :\nTaxonomic assignments were made via sequence similarity searches of the ZOTU sequences against the National Center for Biotechnology Information (NCBI) nucleotide (NCBI nt) database (February 2022). Searches were conducted using the BLASTn algorithm [74,75], with hits required to have a minimum e-score of 1e-20 and cover at least 90% of the query sequence. Automatic assignments were made to the lowest possible taxonomic level where consistent matches occurred, with minimum similarity thresholds of 99%, 97% and 95% for species, genus, and higher-level assignments, respectively. Country-based validation and cross-checking with GBIF (Global Biodiversity Information - https://www.gbif.org/) occurrence records were used to manually improve identifications in cases of equally strong reference matches (rgbif) [76]. Special attention was given to key taxa (highly abundant or known for their important functional diversity), specifically Serranidae, Caranguidae, Holocentridae, Apogonidae, Caesionidae and Muraenidae. For ZOTUs belonging to these families, additional phylogenetic analyses were performed to improve the final taxonomic assignment level of each ZOTUs. ZOTU belonging to these families and their ten best matches from GenBank (excluding matches with query coverage below 90%) were grouped by family in FASTA files. These files were aligned and analyzed in MegaX [77] to achieve phylogenetic inference. The phylogenetical trees were reconstructed using the Neighbor-Joining method with the complete deletion option, and node robustness was assessed throught bootstrap tests (5000 replicates). Taxonomic assignments were deduced from the apical topologies of each tree according to the most common ancestor of the ZOTUs and their closest referent sequences. At the end of the procedure, each ZOTU was assigned to a final taxonomic assignment combining automatic and phylogenetic methods, prioritizing the latter in cases of incongruence.\n\nBased on the functional database of Mouillot et al. [78], five qualitative traits linked to key ecological functions were considered: diet (five categories, see details in S5 Datasets), activity patterns (diurnal, nocturnal, or both), schooling behaviour (five ordinal categories ranging from 1 to < 50 individuals), position in the water column (benthic, pelagic, or both, including bathypelagic). Body shape (eel-like, elongated, fusiform/normal, short and/or deep) was also considered rather than fish size. Functional traits were assigned iteratively, starting with ZOTUs identified at the species level using the database of Mouillot et al. [78]. For ZOTUs assigned only to genus or family, traits were imputed by selecting the most prevalent trait values within their respective taxa. ZOTUs assigned to higher taxonomic levels were excluded from functional analyses. In cases of missing values (NA entries), supplementary information from FishBase (fishbase.org) was used to complete the database, (e.g., Luzonichthys or Diaphus genus). Each ZOTU was ultimately assigned to a functional entity (FE) representing a specific combination of categorical traits, using the mFD package [79].","Diversity analysis :\nUnless stated otherwise, all analyses were performed in R studio using the vegan R package [68]. Diversity analyses were based on ZOTU clusters, enabling the assessment of richness within a dataset containing heterogeneous taxonomic assignment levels. Since different ZOTUs may belong to the same species (genetic polymorphism), ZOTUs were aggregated based on their sequence similarity to approximate species-level clustering. To achieve this, clustering analysis of ZOTUs was performed using the seq_cluster function (R Bioseq package) [81] with the Complete Linkage Clustering algorithm, which computes the maximal object-to-object distance. The similarity threshold was determined empirically based on a customized MiFish sequence dataset of sequences retrieved from GenBank. The optimal threshold was identified using the threshOpt function of the SPIDER package [82], that is useful to assess the barcoding gap by returning the total cumulative errors (false positive and false negative) of identification accuracy for different threshold values (S3 Text for details). Following the clustering analysis, ZOTU occurrences were summed by clusters, generating a new frequency table representing cumulative occurrences of ZOTUs by cluster for each sample. This table was used to assess and compare island diversities. To explore taxonomic and functional diversity, we extrapolated the three orders of Hill numbers, namely richness (q = 0, here defined as the cluster richness), Shannon diversity (q = 1) and Simpson diversity (q = 2), using the iNEXT package [83] with the frequency-incidence data option.\n\nDiversity comparisons between islands were conducted using taxonomic and functional approaches. For each approach, cumulative occurrence tables were calculated for ZOTU by family and by FE, respectively. These tables served to identify taxa and FEs that best characterized the differences between fish assemblages on the two islands. The significance of associations between families or FEs for each island was evaluated using multi-level pattern analysis with the multipatt function from indicspecies R package [84]. The tables were then used to generate two sample distance matrices (i.e., Jaccard and the Bray-Curtis dissimilarity indices). Outputs were analyzed using NMDS, and assemblage differences between islands were tested using PERMANOVA (adonis function), after confirming conditional homogeneity of dispersion using the betadisper function. Families and FEs identified as significantly informative were projected onto multidimensional spaces using the envfit function.\n\nTwo additional sensitivity analyses were performed on PERMANOVA results: (i) excluding the Muraenidae family for both family and FE diversity analyses, given its significance in La Réunion and its singular combination of functional traits; and (ii) removing each functional trait one by one from functional analyses (Table 1 and S4 Table). For sensitivity analysis excluding Muraenidae, the results remained unchanged suggesting that this family alone did not drive taxonomic and functional differences between islands (S4 Table). Similarly, removing individual functional traits did not change the results (S4 Table)."]},"countryCoverage":[],"collections":[],"dataDescriptions":[],"dataLanguage":"eng","pubDate":"2025-07-21T00:00:00.000+00:00","maintenanceUpdateFrequency":"UNKOWN","maintenanceDescription":"","license":"http://creativecommons.org/licenses/by/4.0/legalcode","dwca":{"coreType":"http://rs.tdwg.org/dwc/terms/Occurrence","extensions":["http://rs.gbif.org/terms/1.0/DNADerivedData"],"modified":"2026-10-12T03:45:59.081+00:00"},"category":["eDNA"],"approximateCounts":{"occurrenceCount":1269}}