Feces DNA analyses track the rehabilitation of a free-ranging beluga whale.

Following the sudden appearance, and subsequent efforts to support the survival of a beluga whale (Delphinapterus leucas) speculated to have been previously trained off the coast of Norway, we investigate the animal's ability to readapt to life in the wild. Dietary DNA (dDNA) analysis was used to assess diet throughout this rehabilitation process, and during a return to unassisted foraging and self-feeding. Metabarcoding of feces collected throughout this process, confirmed the diversification of the beluga whale's diet to local prey. These findings are indicative of improved foraging behavior, and the ability of this individual to resume wild foraging following a period of dependency in managed care. New insight of digestion rates, and the time window during which prey detection through dDNA analysis is appropriate was also obtained. Beyond the case study presented here, we demonstrate the power of dDNA analysis as a non-intrusive tool to assess the diet of large mammals and track progress adapting to life in the wild following release from captivity and rehabilitation programs.

Capture by hybridization for full-length barcode-based eukaryotic and prokaryotic biodiversity inventories of deep sea ecosystems.

Biodiversity inventory of marine systems remains limited due to unbalanced access to the three ocean dimensions. The use of environmental DNA (eDNA) for metabarcoding allows fast and effective biodiversity inventory and is forecast as a future biodiversity research and biomonitoring tool. However, in poorly understood ecosystems, eDNA results remain difficult to interpret due to large gaps in reference databases and PCR bias limiting the detection of some major phyla. Here, we aimed to circumvent these limitations by avoiding PCR and recollecting larger DNA fragments to improve assignment of detected taxa through phylogenetic reconstruction. We applied capture by hybridization (CBH) to enrich DNA from deep-sea sediment samples and compared the results with those obtained through an up-to-date metabarcoding PCR-based approach (MTB). Originally developed for bacterial communities and targeting 16S rDNA, the CBH approach was applied to 18S rDNA to improve the detection of species forming benthic communities of eukaryotes, with a particular focus on metazoans. The results confirmed the possibility of extending CBH to metazoans with two major advantages: (i) CBH revealed a broader spectrum of prokaryotic, eukaryotic, and particularly metazoan diversity, and (ii) CBH allowed much more robust phylogenetic reconstructions of full-length barcodes with up to 1900 base pairs. This is particularly important for taxa whose assignment is hampered by gaps in reference databases. This study provides a database and probes to apply 18S CBH to diverse marine systems, confirming this promising new tool to improve biodiversity assessments in data-poor ecosystems such as those in the deep sea.

Metabarcoding confirms the opportunistic foraging behaviour of Atlantic bluefin tuna and reveals the importance of gelatinous prey.

Studies of the diet, feeding habits and trophic activity of top marine predators are essential for understanding their trophodynamics. The main direct method used for such studies thus far has been morphological inventories of stomach contents. This approach presents limitations such as missing gelatinous prey, which are usually digested too quickly to be detectable. Here, we analysed the stomachs of 48 Atlantic bluefin tuna (Thunnus thynnus, approximately 15 to 60 kg, including juveniles and adult fishes) collected from the Mediterranean Sea through the metabarcoding of two gene regions (cytochrome c oxidase subunit I (COI) and the ribosomal 18S-V1V2 region). The identified prey taxa and their relative read abundances (RRAs) estimated using COI results were in line with the findings of morphologically based inventories simultaneously performed on the same set of tuna samples. In both cases (and with the same rankings), the prey taxa included anchovy (Engraulis encrasicolus, here detected in more than 80% of samples, RRA = 43%), sardine (Sardina pilchardus, also approximately 80%, RRA = 30%), sprat (Sprattus sprattus, approximately 66%, RRA = 8%), mackerel (Scomber colias, approximately 44%, RRA = 7%) and cephalopods (approximately 15%, RRA = 1.4%). Another striking result was the detection, based on 18S (with which vertebrates were detected as the most abundant group, RRA = 61.6%), of a high prevalence and diversity of gelatinous organisms (RRA = 27.1%), including cnidarians (6.7%), salps (11.7%), and ctenophores (8.7%), the latter increasing with the size of the predator. These results thus support the hypothesis of the role of gelatinous prey in the diet of Atlantic bluefin tuna, suggesting that this species is even more generalist and opportunistic than previously thought. This study further confirms that DNA metabarcoding can be a powerful tool for assessing the diet and trophodynamics of top marine predators.

Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding.

Environmental DNA metabarcoding is a powerful tool for studying biodiversity. However, bioinformatic approaches need to adjust to the diversity of taxonomic compartments targeted as well as to each barcode gene specificities. We built and tested a pipeline based on read correction with DADA2 allowing analysing metabarcoding data from prokaryotic (16S) and eukaryotic (18S, COI) life compartments. We implemented the option to cluster amplicon sequence variants (ASVs) into operational taxonomic units (OTUs) with swarm, a network-based clustering algorithm, and the option to curate ASVs/OTUs using LULU. Finally, taxonomic assignment was implemented via the Ribosomal Database Project Bayesian classifier (RDP) and BLAST. We validated this pipeline with ribosomal and mitochondrial markers using metazoan mock communities and 42 deep-sea sediment samples. The results show that ASVs and OTUs describe different levels of biotic diversity, the choice of which depends on the research questions. They underline the advantages and complementarity of clustering and LULU-curation for producing metazoan biodiversity inventories at a level approaching the one obtained using morphological criteria. While clustering removes intraspecific variation, LULU effectively removes spurious clusters, originating from errors or intragenomic variability. Swarm clustering affected alpha and beta diversity differently depending on genetic marker. Specifically, d-values > 1 appeared to be less appropriate with 18S for metazoans. Similarly, increasing LULU's minimum ratio level proved essential to avoid losing species in sample-poor data sets. Comparing BLAST and RDP underlined that accurate assignments of deep-sea species can be obtained with RDP, but highlighted the need for a concerted effort to build comprehensive, ecosystem-specific databases.

Publisher Correction: Metabarcoding of marine environmental DNA based on mitochondrial and nuclear genes.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Metabarcoding of marine environmental DNA based on mitochondrial and nuclear genes.

We establish the new approach of environmental DNA (eDNA) analyses for the North Sea. Our study uses a multigene approach, including the mitochondrial cytochrome-c-oxidase subunit I (COI) gene for analyzing species composition and the nuclear hypervariable region V8 of 18S rDNA for analyzing supraspecific biodiversity. A new minibarcode primer (124 bp) was created on the basis of a metazoan COI barcode library with 506 species and tested in silico, in vitro, and in situ. We applied high throughput sequencing to filtrates of 23 near-bottom water samples taken at three seasons from 14 stations. The set of COI primers allowed amplification of mitochondrial minibarcodes for diverse metazoan phyla and the differentiation at the species level for more than 99% of the specimens in the dataset. Our results revealed that the number of sequences is not consistent with proportions in the given DNA mixture. Altogether, environmental sequences could be assigned to 114 species and to 12 metazoan phyla. A spatial distribution of taxa recovered by eDNA was congruent with known distributions. Finally, the successful detection of species and biodiversity depends on a comprehensive sequence reference database. Our study offers a powerful tool for future biodiversity research, including the detection of nonnative species.