Abstract. Nickel (Ni) is an essential micronutrient for marine microorganisms, being involved in enzymes controlling the nitrogen cycle and metabolic responses to oxidative stress. In this study, we examine the covariation between the abundance of Ni-related enzymes and Ni isotope fractionation. To do so, dissolved Ni concentrations and isotope compositions are presented together with metagenomics on samples from the Antarctic Circumnavigation Expedition. Overall, results reveal lower Ni concentrations and higher δ60Ni values in surface waters north of the Sub-Antarctic Front compared to southerly stations. One exception is seen near the high-latitude Mertz Glacier, where the systematics between Ni and δ60Ni better resemble those of low-latitude stations. Relative abundances of urease and Ni-SOD in metagenomes are found to correlate with δ60Ni, potentially suggesting preferential biological uptake of Ni by the organisms using these enzymes. We find a particularly high abundance of urease in diatoms and alphaproteobacteria near the Mertz Glacier, matching the surprisingly high δ60Ni. We thus hypothesise that urea could serve as a nitrogen source for microbial organisms in the late stage of polynya diatom blooms, perhaps causing the observed Ni drawdown and isotope fractionation. This study represents an initial exploration of the influence of biological processes on Ni and δ60Ni distributions. It constitutes a first step towards the further analyses (e.g., culture experiments and metatranscriptomics) needed to determine which exact processes lead to the δ60Ni biogeochemical divide observed between low-latitude and high-latitude waters.

Mixoplankton, marine planktonic protists that combine photo-autotrophy and phago-heterotrophy, play vital roles in marine ecosystems as producers, consumers and nutrient recyclers. However, the environmental drivers of their global distribution remain poorly understood. Here, we utilised global DNA metabarcoding data sourced from the metaPR2 database and classified 47,000 marine protist ASVs (amplicon sequence variants) into four mixoplankton functional types ‒ constitutive (CM), generalist non-constitutive (GNCM), endosymbiotic-specialist non-constitutive (eSNCM) and plastidic-specialist non-constitutive (pSNCM) ‒ and other functional groups (diatoms, non-diatom phytoplankton, protozooplankton, and parasites). We then applied a machine learning-based community clustering method to delineate assemblages, which were subsequently analysed using multivariate and statistical techniques to resolve the spatial distribution and environmental associations of mixoplankton within global protistan communities. Analysis of ASV richness and relative abundance confirmed that mixoplankton are ubiquitous components of protistan communities. Self-organising maps and distance-based redundancy analyses identified communities structured along environmental gradients aligned with classical oceanographic regions, and generalised additive models supported statistical associations with temperature, salinity and nitrate concentration. CM were broadly distributed in oligotrophic waters; eSNCM were restricted to warmer biomes; and GNCM and pSNCM showed niche-specific associations with nutrient regimes. Non-diatom phytoplankton had distributional patterns similar to CM, suggesting functional overlap or shared resource utilisation. In contrast, diatoms were predominantly associated with cold nitrate-rich waters, while protozooplankton and parasites displayed trends generally inverse to those of mixoplankton. Through the integration of metabarcoding data with machine learning, multivariate and statistical approaches, we demonstrate that distinct mixoplankton functional types occupy ecologically differentiated niches governed by temperature, salinity, and nutrient availability. These findings enhance our understanding of mixoplankton ecology and global distribution, helping to establish a foundation for their integration into predictive models of marine ecosystem dynamics.

The Southern Ocean (SO) plays a key role in regulating global biogeochemical cycles and climate, yet microbial genes sustaining its biological activity remain poorly characterized. We introduce a microbial genes collection from 218 metagenomes sampled during the Antarctic Circumnavigation Expedition, the majority of which are missing from functional databases. 38% even lack homologs in current reference marine gene catalogs, defining a singular genetic seascape. We show that SO gene assemblages exhibit a common polar signature with the Arctic Ocean while being structured by water masses at the SO-scale. We analyze genomic markers of diverse SO biomes, focusing on dimethylsulphoniopropionate (DMSP) cleavage by polar-adapted bacteria, organic matter consumption in the blooming Mertz polynya and adaptation to polar conditions in the ubiquitous bacteria Pelagibacter. Our work takes a step towards a comprehensive understanding of SO's plankton ecology and evolution, capturing the current state of the unique microbial diversity in this rapidly changing 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.

Aquatic ecologists face challenges in identifying the general rules of the functioning of ecosystems. A common framework, including freshwater, marine, benthic, and pelagic ecologists, is needed to bridge communication gaps and foster knowledge sharing. This framework should transcend local specificities and taxonomy in order to provide a common ground and shareable tools to address common scientific challenges. Here, we advocate the use of functional trait-based approaches (FTBAs) for aquatic ecologists and propose concrete paths to go forward. Firstly, we propose to unify existing definitions in FTBAs to adopt a common language. Secondly, we list the numerous databases referencing functional traits for aquatic organisms. Thirdly, we present a synthesis on traditional as well as recent promising methods for the study of aquatic functional traits, including imaging and genomics. Finally, we conclude with a highlight on scientific challenges and promising venues for which FTBAs should foster opportunities for future research. By offering practical tools, our framework provides a clear path forward to the adoption of trait-based approaches in aquatic ecology.

Pendant plusieurs siècles, une minorité suédophone a vécu sur les îles et les côtes du nord-ouest de l'Estonie. Dispersés géographiquement et parlant des dialectes non mutuellement intelligibles, les suédophones d'Estonie ont affirmé dans le tournant du xxe siècle une identité nationale suédoise pour devenir les Estlandssvenskar (« Suédois d'Estonie »). Cependant, les bouleversements de la Seconde Guerre mondiale ont provoqué le départ massif des suédophones d'Estonie. Dans cet article, nous nous proposons d'interroger le caractère autochtone de cette population à travers le prisme des définitions critérielles de l'autochtonie présentées entre autres par l'anthropologie et le droit. Pour ce faire, nous nous intéresserons à l'histoire de la minorité en Estonie, à sa géographie, à sa construction identitaire, ainsi qu'aux représentations ethniques endogènes et exogènes de cette population suédophone. Nous constatons alors que le caractère autochtone évolue dans le temps et qu'il est le fruit des rapports politiques d'une minorité socio-culturelle avec un État. En outre, nous notons que la minorité des Estlandssvenskar ne répond plus à certains critères proposés dans les définitions de l'autochtonie. Nous présentons ensuite une classification linguistique des dialectes suédois d'Estonie, appelés estlandssvenska et l'évolution de leurs fonctions sociales, principalement vernaculaires, dans l'espace multilingue que forme l'Estonie en comparaison avec le suédois standard (rikssvenska). Enfin, nous exposons une combinaison de six facteurs qui explique la disparition de l'usage des dialectes estlandssvenska de nos jours.

Tab_PFCLevel_Faureetal2020_PRTable with 233,756 lines corresponding to protein functional clusters, and 67 columns : PFC ID | Size of the PFC | KEGG-based unctional homogeneity | EggNOG-based functional homogeneity | Taxonomical homogeneity at Phylum level | Class level | Order level | Family level | Genus level | MAG level | Cross-validation R squared | Test set predictions R-squared | Importance in RF models of each environmental variable (51 columns) | Position on CCA1 (when available) | Position on CCA2 (when available)Tab_hlePFCLevel_Faureetal2020_PRSame as Tab_PFCLevel_Faureetal2020 but only with the 14,585 lines corresponding to PFCs highly linked to the environment.Tab_DM_PFCLevel_Faureetal2020_PRSame as Tab_PFCLevel_Faureetal2020 but only with the 7,834 lines corresponding to dark PFCs (no functional annotation, no taxonomic annotation under the Phylum level).Tab_S93_PFCLevel_Faureetal2020_PRSame as Tab_PFCLevel_Faureetal2020 but only with the 2,836 lines corresponding to PFCs overabundant at station 93.Tab_S93Pseudoalteromonas_PFCLevel_Faureetal2020_PRSame as Tab_PFCLevel_Faureetal2020 but only with the 1,928 lines corresponding to PFCs overabundant at station 93 that contained at least one protein from a Pseudoalteromonas MAG.Tab_SeqLevel_Faureetal2020_PRTable with 757,457 lines corresponding to all protein sequences included in the sequence similarity network, and 12 columns: Associated PFC | Protein ID | MAG of origin | EggNOG annotation | KEGG annotation | Domain of the MAG of origin | Phylum | Class | Order | Family | Genus | Nucleotidic sequenceTab_hle_SeqLevel_Faureetal2020_PRSame as Tab_SeqLevel_Faureetal2020_withnucl, but only with the 52,536 lines corresponding to proteins from PFCs highly linked to environmental gradients.Tab_DM_SeqLevel_Faureetal2020_PRSame as Tab_SeqLevel_Faureetal2020_withnucl, but only with the 20,552 lines corresponding to proteins from dark PFCs.Tab_S93_SeqLevel_Faureetal2020_PRSame as Tab_SeqLevel_Faureetal2020_withnucl, but only with the 8,364 lines corresponding to proteins from PFCs overabundant at station 93.Tab_S93Pseudoalteromonas_SeqLevel_Faureetal2020_PRSame as Tab_SeqLevel_Faureetal2020_withnucl, but only with the 6,450 lines corresponding to proteins from PFCs overabundant at station 93 that contained at least one protein from a Pseudoalteromonas MAG.TabAbund_CC_mean_CC2Abundance table with 233,756 lines corresponding to all PFCs of size 2 or more in the sequence similarity network, and 93 columns corresponding to the different samples used in our study. Abundances correspond to mean normalized abundance for each PFC, which were used in the statistical analysis presented in the paper main text.TabAbund_CC_NOsum_CC2Abundance table with 757,457 lines corresponding to all proteins involved in PFCs of size 2 or more in the sequence similarity network, and 93 columns corresponding to the different samples used in our study.Proka_nucl.faaFASTA file of the nucleotidic sequences of the 1,914,171 proteins detected in the 885 prokaryotic MAGs used in our study.hlePFCs_nucl.faaFASTA file of the nucleotidic sequences of the 52,536 proteins detected in the 14,585 protein functional clusters highly linked to the environment.DM_PFCs_nucl.faaFASTA file of the nucleotidic sequences of the 20,552 proteins detected in the 7,834 protein functional clusters associated to microbial dark matter.S93_PFCs_nucl.faaFASTA file of the nucleotidic sequences of the 8,364 proteins detected in PFCs overabundant at station 93.S93Pseudoalteromonas_PFC_nucl.faaFASTA file of the 6,450 proteins from PFCs overabundant at station 93 that contained at least one protein from a Pseudoalteromonas MAG.Proka_prot.faaFASTA file of the proteic sequences of the 1,914,171 proteins detected in the 885 prokaryotic MAGs used in our study.Envi_context_Faure_et_al_2020All environmental data used in random forest models.Github_Data_Faure.tar.gzArchive containing raw files necessary to reproduce our results using R codes available at https://github.com/EmileFaure/MAGsProteinFunctionalClusters.

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.

Apport des données méta-omiques dans la détection et la quantification des traits fonctionnels au sein des écosystèmes planctoniques Dans la plupart des modèles biogéochimiques, la diversité planctonique est représentée à l'aide de types planctoniques classant les organismes selon leurs capacités fonctionnelles, ou de traits fonctionnels mesurables au niveau individuel. Un choix a priori des types planctoniques ou traits fonctionnels considérés est nécessaire, pouvant conduire à des représentations simplifiées de la diversité planctoniques dans ces modèles. Des quantités inédites de données méta-omiques ont récemment été collectées sur les communautés planctoniques à l’échelle globale, et mon objectif au cours de cette thèse fut de déterminer comment utiliser ces données méta-omiques afin de quantifier la distribution de traits fonctionnels dans l’environnement. Je présente d'abord comment les données de métabarcoding d’une part et les marqueurs génomiques fonctionnels d’autre part peuvent être utilisées pour décrire et quantifier des traits choisis a priori, identifiant les limites et les avantages des deux types de données. Je présente ensuite une approche permettant de faire émerger des familles protéiques putatives pouvant être associées à des traits fonctionnels au sein des données méta-omiques, sans choix a priori. En quantifiant la réponse de ces familles aux gradients physico-chimiques dans l’océan global, je montre comment cette approche pourrait permettre de prédire la composition fonctionnelle des communautés planctoniques à partir de données environnementales. Enfin, je discute du potentiel des données méta-omiques comme outil pour représenter la diversité des communautés planctoniques de manière réaliste dans les modèles biogéochimiques.

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.