Sedimentary ancient DNA sequences reveal marine ecosystem shifts and indicator taxa for glacial-interglacial sea ice conditions

Sedimentary ancient DNA (sedaDNA) analysis is a promising new approach for reconstructing the impact of past climate and environmental changes on marine paleobiodiversity. By recovering, amplifying, and sequencing taxonomically informative sedaDNA fragments preserved in sediments, it is possible to assess the response of a broad range of eukaryote taxa, including non-fossilizing lineages, to past climate change. Here we present a unique marine derived sedaDNA metabarcoding record, spanning the penultimate glacial-interglacial transition across Marine Isotope Stages 6 to 5d (>135–107 ka) from an Eirik Drift core-site in the Labrador Sea. We identified a range of marine groups including dinoflagellates, diatoms, coccolithophores, chlorophytes, and copepods. There were representatives of primary/secondary producers, grazers, and parasites, which may represent remnants of complex ecosystems and ancient food webs. There were significant biodiversity shifts following the penultimate deglaciation and changing sea ice conditions throughout the Last Interglacial. These shifts reflected the striking increase in community richness during periods of seasonal sea ice and reduction under extensive perennial sea ice cover and open ocean. We identify two potential sedaDNA indicator taxa sequences associated with past seasonal sea ice which are most likely pico-eukaryote representatives of Micromonas and Pyramimonas, both green algae with known sea ice associations in modern ecosystems. Our work demonstrates the importance of high resolution marine sedaDNA metabarcoding for unravelling climate-ecosystem linkages and strengthens the potential of sedaDNA signals for past sea ice reconstructions through indicator sequences.publishedVersio

Sedimentary ancient DNA: a new paleogenomic tool for reconstructing the history of marine ecosystems

Sedimentary ancient DNA (sedaDNA) offers a novel retrospective approach to reconstructing the history of marine ecosystems over geological timescales. Until now, the biological proxies used to reconstruct paleoceanographic and paleoecological conditions were limited to organisms whose remains are preserved in the fossil record. The development of ancient DNA analysis techniques substantially expands the range of studied taxa, providing a holistic overview of past biodiversity. Future development of marine sedaDNA research is expected to dramatically improve our understanding of how the marine biota responded to changing environmental conditions. However, as an emerging approach, marine sedaDNA holds many challenges, and its ability to recover reliable past biodiversity information needs to be carefully assessed. This review aims to highlight current advances in marine sedaDNA research and to discuss potential methodological pitfalls and limitations

Instance nationale et multi-communauté de DIRAC pour France Grilles

DIRAC [DIRAC] [TSA-08] is a software framework for building distributed computing systems. It was primarily designed forthe needs of the LHCb [LHCb] Collaboration, and is now used by many other communities within EGI [EGI] as a primary wayof accessing grid resources. In France, dedicated instances of the service have been deployed in different locations toanswer specific needs. Building upon this existing expertise, France Grilles [FG] initiated last year a project to deploy anational, multi-community instance in order to share expertise and provide a consistent high-quality service. After describingDIRAC main aims and functionalities, this paper presents the motivations for such a project, as well as the wholeorganizational and technical process that led to the establishment of a production instance that already serves 13communities: astro.vo.eu-egee.org, biomed, esr, euasia, gilda, glast.org, prod.vo.eu-eela.eu, superbvo.org,vo.formation.idgrilles.fr, vo.france-asia.org, vo.france-grilles.fr, vo.msfg.fr and vo.mcia.fr

Arctic Paleoceanography Cruise KH21-234 with R/V Kronprins Haakon

We set sail from Longyearbyen on 30.6.2021 to collect surface sediments, long sediment archives, water and plankton samples. The study area is located north of Svalbard, within the seasonal and permanent sea ice covered Arctic Ocean. We took stations N of Svalbard, near Nordaustlandet, Sophia Basin, Yermak Plateau and on the shelf east of Svalbard. In total, we had 52 stations. We deployed the multicorer at least once at every station and sampled the core tops already onboard. These samples will be included in the Arctic Surface Sediment DNA Database, which we will use to establish new aDNA based sea ice proxies. We recovered gravity cores from 12 stations that can be used to reconstruct the Arctic sea ice history in the Holocene, last glacial and likely also Last Interglacial. We collected ice and water and filtered these for eDNA and biomarkers, and water for tracing the isotope signal of the different water masses in the region (Atlantic Water, Polar Water).publishedVersio

Ecosystems monitoring powered by environmental genomics: a review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or “in development”, hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.publishedVersio

Eukaryotic biodiversity and spatial patterns in the Clarion-Clipperton Zone and other Abyssal regions: Insights from sediment DNA and RNA metabarcoding

The abyssal seafloor is a mosaic of highly diverse habitats that represent the least known marine ecosystems on Earth. Some regions enriched in natural resources, such as polymetallic nodules in the Clarion-Clipperton Zone (CCZ), attract much interest because of their huge commercial potential. Since nodule mining will be destructive, baseline data are necessary to measure its impact on benthic communities. Hence, we conducted an environmental DNA and RNA metabarcoding survey of CCZ biodiversity targeting microbial and meiofaunal eukaryotes that are the least known component of the deep-sea benthos. We analyzed two 18S rRNA gene regions targeting eukaryotes with a focus on Foraminifera (37F) and metazoans (V1V2), sequenced from 310 surface-sediment samples from the CCZ and other abyssal regions. Our results confirm huge unknown deep-sea biodiversity. Over 60% of benthic foraminiferal and almost a third of eukaryotic operational taxonomic units (OTUs) could not be assigned to a known taxon. Benthic Foraminifera are more common in CCZ samples than metazoans and dominated by clades that are only known from environmental surveys. The most striking results are the uniqueness of CCZ areas, both datasets being characterized by a high number of OTUs exclusive to the CCZ, as well as greater beta diversity compared to other abyssal regions. The alpha diversity in the CCZ is high and correlated with water depth and terrain complexity. Topography was important at a local scale, with communities at CCZ stations located in depressions more diverse and heterogeneous than those located on slopes. This could result from eDNA accumulation, justifying the interim use of eRNA for more accurate biomonitoring surveys. Our descriptions not only support previous findings and consolidate our general understanding of deep-sea ecosystems, but also provide a data resource inviting further taxon-specific and large-scale modeling studies. We foresee that metabarcoding will be useful for deep-sea biomonitoring efforts to consider the diversity of small taxa, but it must be validated based on ground truthing data or experimental studies

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

COBRA Master Class: Providing deep-sea expedition leadership training to accelerate early career advancement

Leading deep-sea research expeditions requires a breadth of training and experience, and the opportunities for Early Career Researchers (ECRs) to obtain focused mentorship on expedition leadership are scarce. To address the need for leadership training in deep-sea expeditionary science, the Crustal Ocean Biosphere Research Accelerator (COBRA) launched a 14-week virtual Master Class with both synchronous and asynchronous components to empower students with the skills and tools to successfully design, propose, and execute deep-sea oceanographic field research. The Master Class offered customized and distributed training approaches and created an open-access syllabus with resources, including reading material, lectures, and on-line resources freely-available on the Master Class website (cobra.pubpub.org). All students were Early Career Researchers (ECRs, defined here as advanced graduate students, postdoctoral scientists, early career faculty, or individuals with substantial industry, government, or NGO experience) and designated throughout as COBRA Fellows. Fellows engaged in topics related to choosing the appropriate deep-sea research asset for their Capstone “dream cruise” project, learning about funding sources and how to tailor proposals to meet those source requirements, and working through an essential checklist of pre-expedition planning and operations. The Master Class covered leading an expedition at sea, at-sea operations, and ship-board etiquette, and the strengths and challenges of telepresence. It also included post-expedition training on data management strategies and report preparation and outputs. Throughout the Master Class, Fellows also discussed education and outreach, international ocean law and policy, and the importance and challenges of team science. Fellows further learned about how to develop concepts respectfully with regard to geographic and cultural considerations of their intended study sites. An assessment of initial outcomes from the first iteration of the COBRA Master Class reinforces the need for such training and shows great promise with one-quarter of the Fellows having submitted a research proposal to national funding agencies within six months of the end of the class. As deep-sea research continues to accelerate in scope and speed, providing equitable access to expedition training is a top priority to enable the next generation of deep-sea science leadership

The future of biotic indices in the ecogenomic era: Integrating (e)DNA metabarcoding in biological assessment of aquatic ecosystems

The bioassessment of aquatic ecosystems is currently based on various biotic indices that use the occurrence and/or abundance of selected taxonomic groups to define ecological status. These conventional indices have some limitations, often related to difficulties in morphological identification of bioindicator taxa. Recent development of DNA barcoding and metabarcoding could potentially alleviate some of these limitations, by using DNA sequences instead of morphology to identify organisms and to characterize a given ecosystem. In this paper, we review the structure of conventional biotic indices, and we present the results of pilot metabarcoding studies using environmental DNA to infer biotic indices. We discuss the main advantages and pitfalls of metabarcoding approaches to assess parameters such as richness, abundance, taxonomic composition and species ecological values, to be used for calculation of biotic indices. We present some future developments to fully exploit the potential of metabarcoding data and improve the accuracy and precision of their analysis. We also propose some recommendations for the future integration of DNA metabarcoding to routine biomonitoring programs.info:eu-repo/semantics/publishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets