Diversity and Biogeography of Bathyal and Abyssal Seafloor Bacteria and Archaea Along a Mediterranean-Atlantic Gradient.

Seafloor sediments cover the majority of planet Earth and microorganisms inhabiting these environments play a central role in marine biogeochemical cycles. Yet, description of the biogeography and distribution of sedimentary microbial life is still too sparse to evaluate the relative contribution of processes driving this distribution, such as the levels of drift, connectivity, and specialization. To address this question, we analyzed 210 archaeal and bacterial metabarcoding libraries from a standardized and horizon-resolved collection of sediment samples from 18 stations along a longitudinal gradient from the eastern Mediterranean to the western Atlantic. Overall, we found that biogeographic patterns depended on the scale considered: while at local scale the selective influence of contemporary environmental conditions appeared strongest, the heritage of historic processes through dispersal limitation and drift became more apparent at regional scale, and ended up superseding contemporary influences at inter-regional scale. When looking at environmental factors, the structure of microbial communities was correlated primarily with water depth, with a clear transition between 800 and 1,200 meters below sea level. Oceanic basin, water temperature, and sediment depth were other important explanatory parameters of community structure. Finally, we propose increasing dispersal limitation and ecological drift with sediment depth as a probable factor for the enhanced divergence of deeper horizons communities.

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.

Evaluating sediment and water sampling methods for the estimation of deep-sea biodiversity using environmental DNA.

Despite representing one of the largest biomes on earth, biodiversity of the deep seafloor is still poorly known. Environmental DNA metabarcoding offers prospects for fast inventories and surveys, yet requires standardized sampling approaches and careful choice of environmental substrate. Here, we aimed to optimize the genetic assessment of prokaryote (16S), protistan (18S V4), and metazoan (18S V1-V2, COI) communities, by evaluating sampling strategies for sediment and aboveground water, deployed simultaneously at one deep-sea site. For sediment, while size-class sorting through sieving had no significant effect on total detected alpha diversity and resolved similar taxonomic compositions at the phylum level for all markers studied, it effectively increased the detection of meiofauna phyla. For water, large volumes obtained from an in situ pump (~ 6000 L) detected significantly more metazoan diversity than 7.5 L collected in sampling boxes. However, the pump being limited by larger mesh sizes (> 20 µm), only captured a fraction of microbial diversity, while sampling boxes allowed access to the pico- and nanoplankton. More importantly, communities characterized by aboveground water samples significantly differed from those characterized by sediment, whatever volume used, and both sample types only shared between 3 and 8% of molecular units. Together, these results underline that sediment sieving may be recommended when targeting metazoans, and aboveground water does not represent an alternative to sediment sampling for inventories of benthic diversity.

Global marine biodiversity in the context of achieving the Aichi Targets: ways forward and addressing data gaps.

In 2010, the Conference of the Parties of the Convention on Biological Diversity agreed on the Strategic Plan for Biodiversity 2011-2020 in Aichi Prefecture, Japan. As this plan approaches its end, we discussed whether marine biodiversity and prediction studies were nearing the Aichi Targets during the 4th World Conference on Marine Biodiversity held in Montreal, Canada in June 2018. This article summarises the outcome of a five-day group discussion on how global marine biodiversity studies should be focused further to better understand the patterns of biodiversity. We discussed and reviewed seven fundamental biodiversity priorities related to nine Aichi Targets focusing on global biodiversity discovery and predictions to improve and enhance biodiversity data standards (quantity and quality), tools and techniques, spatial and temporal scale framing, and stewardship and dissemination. We discuss how identifying biodiversity knowledge gaps and promoting efforts have and will reduce such gaps, including via the use of new databases, tools and technology, and how these resources could be improved in the future. The group recognised significant progress toward Target 19 in relation to scientific knowledge, but negligible progress with regard to Targets 6 to 13 which aimed to safeguard and reduce human impacts on biodiversity.