AtlantECO-KER-IM-1

Mission Microbiomes AtlantECO (MMA) is an international study of the most fundamental fabric of the ocean — the ocean microbiome — aiming to understand its structure, functioning and connectivity in the Atlantic Ocean. It is an initiative of the European Union’s research and innovation project AtlantECO, which aims at sharing capacity along and across the Atlantic, and providing knowledge-based resources to help design policies for the management and protection of Atlantic Ecosystem Services. The success and legacy of MMA on science and society relies on the fair, open and inclusive collaboration among its Partners, and the exploitation of jointly owned MMA Results. The adoption of the present Data Sharing and Publication Best Practices is therefore essential to achieve the scientific objectives of MMA and maximise their impacts.

The main computational goals of AtlantECO for Advances in Systems Ecology were to develop cutting edge network analysis methods, to define an all-Atlantic interactome and ecological niches, and to provide indicators of ecosystem stability and sensitivity to environmental stressors and drivers. Here, we reviewed the literature for ecological network reconstruction methods from several disciplines (microbiology, ecology, bioinformatics). We built a computational pipeline for the inference of species ecological networks from heterogeneous data types, combining statistical and ecological metrics, as well as probabilistic and machine learning algorithms. Reference databases of known ecological interactions obtained from the literature can be used to benchmark and validate inferred networks. This review of network inference algorithms is accompanied by a computational workflow: AtlantEcoNet (publicly available at: https://gitlab.univ-nantes.fr/mbudinich/atlanteconet), which builds upon existing software by integrating a selection of complementary methods for ecological network inference, analysis, and validation. This workflow was used by AtlantECO to build a plankton ecological network from omics data compiled for the Atlantic Ocean.

The library provides a shortlist of enzyme candidates obtained from data mining for later experimental characterisation. Enzymes candidates have potential for industrial applications, such as molecular diagnostics and bioremediation, as well as potentially new bioactive compounds that may support the growth of the biotechnological and pharmaceutical industry. Candidate enzymes may also contribute to research towards a better understanding of biodiversity, with applications in environmental and ecological conservation.

The quantitative description of marine systems is constrained by a major issue of scale separation: phytoplankton production processes occur at sub-centimeter scales, while the contribution to the Earth's biogeochemical cycles is expressed at much larger scales, up to the planetary one. In spite of vastly improved computing power and observational capabilities, the modeling approach has remained anchored to an old view that sees the microscales as unable to substantially affect larger ones. The lack of a widespread theoretical appreciation of the interactions between vastly different scales has led to the proliferation of numerical models with uncertain predictive capabilities. In this paper, we use the phenology of phytoplankton blooms as one example of a macroscopic ecosystem feature affected by microscale interactions. We describe two distinct mechanisms that produce patchiness within a productive water column: turbulent entrainment of less-productive water at the base of the mixed layer, and stirring by slow turbulence of a vertical phytoplankton gradient sustained by depth-dependent light availability. In current eddy-diffusive models, patchiness produced in this way is wiped out very rapidly, because the time scales of irreversible mixing largely overlap those of mechanical stirring. We propose a novel Lagrangian modeling framework that allows for the existence of microscale patchiness, even when that is not fully resolved. We show, with a mixture of theoretical arguments and numerical simulations of increasing realism, how the presence of patchiness, in turn, affects larger-scale properties, demonstrating that the timing of phytoplankton blooms and vertical variability of chlorophyll in the oceanic upper layers is determined by the mutual interplay between the stirring, mixing and growing processes.

The present deliverable (D4.4) reports on AtlantECO’s Handbook of Standards and Best Practices. It was developed throughout the project, and its content is presented in three deliverables (D4.1, D4.2 & D4.4), which are all publicly accessible under a single DOI (https://doi.org/10.5281/zenodo.4897860) and also available via UNESCO’s Ocean Best Practices. AtlantECO generated new observations as part of the project’s sampling activities in synergy with other European and International projects (see deliverable D4.7). The Handbook provides a set of Standard Operating Procedures (SOPs) for methodologies used across these activities to study the Ocean microbiome. These SOPs also constitute a basis for comparing protocols and aggregating results from different observation programmes. The SOPs are generic and describe key methodological concepts, materials and procedural steps, which gives the different observation programmes enough flexibility to adopt the standards and best practices and adapt the SOPs to their specific context.