Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring

Correspondence analysis representing the distribution of the samples according to the presence/absence of fungal Molecular Operational Taxonomic Units (MOTUs).

<p>There were 512 phyllosphere-associated MOTUs (A), 472 root-associated MOTUs (B), and 120 root-associated EcM MOTUs (C). Samples were from the Alps (closed dots), the Pyrenees (filled triangles) or the Vosges (plus sign) with different colours meaning different sites. Percentage of variance into brackets.</p

Relationships between potential factors affecting leaf and root-associated fungal composition.

<p>Permutational multivariate analysis of variance of the compositional dissimilarity.</p

Relationships between fungal richness and mean annual temperature.

<p>Fungal richness from the phyllosphere (A, C, E) or associated with the fine-roots (B, D, F), either total richness (A, B), Ascomycetes richness (C, D) or Basidiomycete richness (E, F). The richness was estimated from rarefaction at a sequencing depth of 1 400 and 500 sequences per leaf and root samples, respectively. Samples corresponded to the Alps (closed dots), the Pyrenees (filled triangles) or the Vosges (plus sign) with different colours meaning different sites.</p

Relationships between potential factors affecting leaf and root-associated fungal richness.

<p>The T-values (p-values) of the linear mixed model are presented for the variables tested and the standard deviations (% of the total variance explained [random+residual]) are presented for the random factors. Significant p-values in bold at 5%. The richness was estimated at 1 400 and 500 sequences per leaf and root samples respectively.</p><p>* Standard deviations associated with the region and site nested within region as random factors (% of the total random plus residual variance explained).</p

Environmental data of the three elevation gradients.

<p>The element contents in soil and the pH were averaged per site for clarity purpose but were measured in the three plots per site.</p

Table_1_COBRA Master Class: Providing deep-sea expedition leadership training to accelerate early career advancement.docx

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.</p