Search

Abstract Fram Strait, the gateway between the Arctic and Atlantic Oceans, is undergoing major climate change‐induced physical and biological transformations. In particular, rapid warming and ongoing “Atlantification” are driving species range shifts and altering food web structures in the Arctic. Understanding and predicting the consequences of these processes on future ecosystems requires detailed assessments of local and pelagic biodiversity. Gelatinous zooplankton (GZP) is an important component of pelagic communities, and recent evidence indicates that such communities are undergoing major changes in the Fram Strait. However, as sampling GZP is challenging, they are regularly underestimated in biodiversity, distribution, and abundance. To overcome this and address existing ecological knowledge gaps, we investigated patterns of pelagic metazoan diversity in Fram Strait using environmental DNA (eDNA) metabarcoding of the cytochrome c oxidase I (COI) gene. We successfully detected a broad range of taxa from the marine metazoan and GZP communities across sampling locations and ocean depth zones. We demonstrate the vertical structuring of diversity and elucidate relationships between taxa and water mass indicators, such as salinity and temperature. Furthermore, when comparing eDNA data with net and video transect data for GZP at the same period and location, we found that eDNA uncovered a higher number of taxa, including several that were not detected by the other methods. This study is a contribution to the formation of baseline Arctic GZP biodiversity datasets, as well as future research on changing marine metazoan biodiversity and community composition.

Summary: Metabarcoding techniques are revolutionizing studies of marine biodiversity. They can be used for monitoring non-indigenous species (NIS) in ports and harbors. However, they are often biased by inconsistent sampling methods and incomplete reference databases. Logistic constraints in ports prompt the development of simple, easy-to-deploy samplers. We tested a new device called polyamide mesh for ports organismal monitoring (POMPOM) with a high surface-to-volume ratio. POMPOMS were deployed inside a fishing and recreational port in the Mediterranean alongside conventional settlement plates. We also compiled a curated database with cytochrome oxidase (COI) sequences of Mediterranean NIS. COI metabarcoding of the communities settled in the POMPOMs captured a similar biodiversity than settlement plates, with shared molecular operational units (MOTUs) representing ca. 99% of reads. 38 NIS were detected in the port accounting for ca. 26% of reads. POMPOMs were easy to deploy and handle and provide an efficient method for NIS surveillance.

Against the accelerating pace of worldwide species extinction, reliable biodiversity assessments are critical, both as baselines and to track potential declines. DNA metabarcoding techniques allow for fast and comprehensive assessment of biodiversity in both terrestrial and marine habitats. However, these methods need to be adapted and standardised for each ecosystem in order to be effective. Seagrass meadows are among the most diverse marine habitats and are irreplaceable in terms of the ecosystem services they provide, yet metabarcoding has never been implemented for these systems. In this study, we developed and tested a protocol for metabarcoding the eukaryotic community of meadows of the iconic species, Posidonia oceanica L. (Delile). This seagrass is the main habitat-forming species in Mediterranean coastal waters and is known for its high diversity due to the structural complexity of its canopy and rhizome structures. This habitat is experiencing a range-wide retreat, and there is an urgent need for fast and efficient methods for its biomonitoring and detection of early changes. Our proposed method involves direct sampling of the community, collecting and processing the leaves and rhizome strata separately. To test the utility of the method in distinguishing between different meadow conditions, we sampled two distinct meadows that differ in their prevailing wind and surge conditions, and a nearby rocky reef for comparison. We then adapted a method and pipeline for COI metabarcoding using generalist primers that target the eukaryote diversity present. We detected a high diversity in the two meadows analysed (3,350 molecular operational taxonomic units, dominated by Metazoa and Archaeplastida) and a clear differentiation of the seagrass samples from those of the nearby rocky reefs. The leaves and rhizomes harboured clearly distinct assemblages, and differences were also detected between the two meadows sampled. This new tool has the potential to deliver big biodiversity data for seagrass habitats in a fast and efficient way, which is crucial for the implementation of protection and management measures for this key coastal habitat.

Abstract Background The recent blooming of metabarcoding applications to biodiversity studies comes with some relevant methodological debates. One such issue concerns the treatment of reads by denoising or by clustering methods, which have been wrongly presented as alternatives. It has also been suggested that denoised sequence variants should replace clusters as the basic unit of metabarcoding analyses, missing the fact that sequence clusters are a proxy for species-level entities, the basic unit in biodiversity studies. We argue here that methods developed and tested for ribosomal markers have been uncritically applied to highly variable markers such as cytochrome oxidase I (COI) without conceptual or operational (e.g., parameter setting) adjustment. COI has a naturally high intraspecies variability that should be assessed and reported, as it is a source of highly valuable information. We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines. Results Using a COI dataset from benthic marine communities, we compared two denoising procedures (based on the UNOISE3 and the DADA2 algorithms), set suitable parameters for denoising and clustering, and applied these steps in different orders. Our results indicated that the UNOISE3 algorithm preserved a higher intra-cluster variability. We introduce the program DnoisE to implement the UNOISE3 algorithm taking into account the natural variability (measured as entropy) of each codon position in protein-coding genes. This correction increased the number of sequences retained by 88%. The order of the steps (denoising and clustering) had little influence on the final outcome. Conclusions We highlight the need for combining denoising and clustering, with adequate choice of stringency parameters, in COI metabarcoding. We present a program that uses the coding properties of this marker to improve the denoising step. We recommend researchers to report their results in terms of both denoised sequences (a proxy for haplotypes) and clusters formed (a proxy for species), and to avoid collapsing the sequences of the latter into a single representative. This will allow studies at the cluster (ideally equating species-level diversity) and at the intra-cluster level, and will ease additivity and comparability between studies.

DNA metabarcoding is broadly used in biodiversity studies encompassing a wide range of organisms. Erroneous amplicons, generated during amplification and sequencing procedures, constitute one of the major sources of concern for the interpretation of metabarcoding results. Several denoising programs have been implemented to detect and eliminate these errors. However, almost all denoising software currently available has been designed to process non-coding ribosomal sequences, most notably prokaryotic 16S rDNA. The growing number of metabarcoding studies using coding markers such as COI or RuBisCO demands a re-assessment and calibration of denoising algorithms. Here we present DnoisE, the first denoising program designed to detect erroneous reads and merge them with the correct ones using information from the natural variability (entropy) associated to each codon position in coding barcodes. We have developed an open-source software using a modified version of the UNOISE algorithm. DnoisE implements different merging procedures as options, and can incorporate codon entropy information either retrieved from the data or supplied by the user. In addition, the algorithm of DnoisE is parallelizable, greatly reducing runtimes on computer clusters. Our program also allows different input file formats, so it can be readily incorporated into existing metabarcoding pipelines.

Sponges have recently been proposed as ideal candidates to act as natural samplers for environmental DNA due to their efficiency in filtering water. However, validation of the usefulness of DNA recovered from sponges to reveal vertebrate biodiversity patterns in Marine Protected Areas is still needed. Additionally, nothing is known about how different sponge species and morphologies influence the capture of environmental DNA and whether biodiversity patterns obtained from sponges are best described by quantitative or qualitative measures. In this study, we amplified and sequenced a vertebrate specific 12S barcode with a set of universal PCR primers (MiFish) for metabarcoding environmental DNA from fishes, to unveil fine-scale patterns of fish communities from natural-sampler DNA retrieved from 64 sponges (16 species) located in eutrophic and well-preserved coral reefs in Nha Trang Bay (central Vietnam). Ninety tropical fish species were identified from the sponges, corresponding to one third of the total local ichthyofauna reported from previous extensive conventional surveys. Significant differentiation in fish communities between eutrophic and well-preserved environments was observed, albeit eutrophication only explained a modest proportion of the variation between fish communities. Differences in efficiency of capturing fish environmental DNA among sponge species or morphologies were not observed. Overall, the majority of detected fish species corresponded to reef-associated small-sized species, as expected in coral reefs environments. Remarkably, pelagic, migratory, and deep-sea fish species were also recovered from sponge tissues, pointing out the ability of sponge natural sampled DNA to detect fishes that were not permanently associated to the biomes where the sponges were sampled. These results highlight the suitability of natural samplers as a cost-effective way to assess vertebrate diversity in hyper-diverse environments.

Among the complex ecosystems and habitats that form the deep sea, submarine canyons and open slope systems are regarded as potential hotspots of biodiversity. We assessed the spatial and temporal patterns of biodiversity in sediment communities of a NW Mediterranean Canyon and its adjacent open slope (Blanes Canyon) with DNA metabarcoding. We sampled three layers of sediment and four different depths (900−1750 m) at two seasons, and used a fragment of the mitochondrial gene cytochrome c oxidase subunit I (COI) as a metabarcoding marker. The final dataset contained a total of 15,318 molecular operational taxonomic units (MOTUs). Metazoa, Stramenopiles and Archaeplastida were the dominant taxa and, within metazoans, Arthropoda, Nematoda and Cnidaria were the most diverse. There was a trend towards decreasing MOTU richness and diversity in the first few cm (1 to 5) of the sediment, with only 26.3% of the MOTUs shared across sediment layers. Our results show the presence of heterogeneous communities in the studied area, which was significantly different between zones, depths and seasons. We compared our results with the ones presented in a previous study, obtained using the v7 region of the 18S rRNA gene in the same samples. There were remarkable differences in the total number of MOTUs and in the most diverse taxa. COI recovered a higher number of MOTUs, but more remained unassigned taxonomically. However, the broad spatio-temporal patterns elucidated from both datasets coincided, with both markers retrieving the same ecological information. Our results showed that COI can be used to accurately characterize the studied communities and constitute a high-resolution method to detect ecological shifts. We also highlight that COI reference databases for deep-sea organisms have important gaps, and their completeness is essential in order to successfully apply metabarcoding techniques.