The Mixoplankton Database (MDB): Diversity of photo-phago-trophic plankton in form, function, and distribution across the global ocean.

Protist plankton are major members of open-water marine food webs. Traditionally divided between phototrophic phytoplankton and phagotrophic zooplankton, recent research shows many actually combine phototrophy and phagotrophy in the one cell; these protists are the "mixoplankton." Under the mixoplankton paradigm, "phytoplankton" are incapable of phagotrophy (diatoms being exemplars), while "zooplankton" are incapable of phototrophy. This revision restructures marine food webs, from regional to global levels. Here, we present the first comprehensive database of marine mixoplankton, bringing together extant knowledge of the identity, allometry, physiology, and trophic interactivity of these organisms. This mixoplankton database (MDB) will aid researchers that confront difficulties in characterizing life traits of protist plankton, and it will benefit modelers needing to better appreciate ecology of these organisms with their complex functional and allometric predator-prey interactions. The MDB also identifies knowledge gaps, including the need to better understand, for different mixoplankton functional types, sources of nutrition (use of nitrate, prey types, and nutritional states), and to obtain vital rates (e.g. growth, photosynthesis, ingestion, factors affecting photo' vs. phago' -trophy). It is now possible to revisit and re-classify protistan "phytoplankton" and "zooplankton" in extant databases of plankton life forms so as to clarify their roles in marine ecosystems.

Towards omics-based predictions of planktonic functional composition from environmental data.

Marine microbes play a crucial role in climate regulation, biogeochemical cycles, and trophic networks. Unprecedented amounts of data on planktonic communities were recently collected, sparking a need for innovative data-driven methodologies to quantify and predict their ecosystemic functions. We reanalyze 885 marine metagenome-assembled genomes through a network-based approach and detect 233,756 protein functional clusters, from which 15% are functionally unannotated. We investigate all clusters' distributions across the global ocean through machine learning, identifying biogeographical provinces as the best predictors of protein functional clusters' abundance. The abundances of 14,585 clusters are predictable from the environmental context, including 1347 functionally unannotated clusters. We analyze the biogeography of these 14,585 clusters, identifying the Mediterranean Sea as an outlier in terms of protein functional clusters composition. Applicable to any set of sequences, our approach constitutes a step towards quantitative predictions of functional composition from the environmental context.

Mixotrophic protists display contrasted biogeographies in the global ocean.

Mixotrophy, or the ability to acquire carbon from both auto- and heterotrophy, is a widespread ecological trait in marine protists. Using a metabarcoding dataset of marine plankton from the global ocean, 318,054 mixotrophic metabarcodes represented by 89,951,866 sequences and belonging to 133 taxonomic lineages were identified and classified into four mixotrophic functional types: constitutive mixotrophs (CM), generalist non-constitutive mixotrophs (GNCM), endo-symbiotic specialist non-constitutive mixotrophs (eSNCM), and plastidic specialist non-constitutive mixotrophs (pSNCM). Mixotrophy appeared ubiquitous, and the distributions of the four mixotypes were analyzed to identify the abiotic factors shaping their biogeographies. Kleptoplastidic mixotrophs (GNCM and pSNCM) were detected in new zones compared to previous morphological studies. Constitutive and non-constitutive mixotrophs had similar ranges of distributions. Most lineages were evenly found in the samples, yet some of them displayed strongly contrasted distributions, both across and within mixotypes. Particularly divergent biogeographies were found within endo-symbiotic mixotrophs, depending on the ability to form colonies or the mode of symbiosis. We showed how metabarcoding can be used in a complementary way with previous morphological observations to study the biogeography of mixotrophic protists and to identify key drivers of their biogeography.