Morphological and taxonomic diversity of mesozooplankton is an important driver of carbon export fluxes in the ocean.

Mesozooplankton is a very diverse group of small animals ranging in size from 0.2 to 20 mm not able to swim against ocean currents. It is a key component of pelagic ecosystems through its roles in the trophic networks and the biological carbon pump. Traditionally studied through microscopes, recent methods have been however developed to rapidly acquire large amounts of data (morphological, molecular) at the individual scale, making it possible to study mesozooplankton using a trait-based approach. Here, combining quantitative imaging with metabarcoding time-series data obtained in the Sargasso Sea at the Bermuda Atlantic Time-series Study (BATS) site, we showed that organisms' transparency might be an important trait to also consider regarding mesozooplankton impact on carbon export, contrary to the common assumption that just size is the master trait directing most mesozooplankton-linked processes. Three distinct communities were defined based on taxonomic composition, and succeeded one another throughout the study period, with changing levels of transparency among the community. A co-occurrences' network was built from metabarcoding data revealing six groups of taxa. These were related to changes in the functioning of the ecosystem and/or in the community's morphology. The importance of Diel Vertical Migration at BATS was confirmed by the existence of a group made of taxa known to be strong migrators. Finally, we assessed if metabarcoding can provide a quantitative approach to biomass and/or abundance of certain taxa. Knowing more about mesozooplankton diversity and its impact on ecosystem functioning would allow to better represent them in biogeochemical models.

Morphological diversity increases with decreasing resources along a zooplankton time series.

Biodiversity is studied notably because of its reciprocal relationship with ecosystem functions such as production. Diversity is traditionally described from a taxonomic, genetic or functional point of view but the diversity in organism morphology is seldom explicitly considered, except for body size. We describe morphological diversity of marine zooplankton seasonally and over 12 years using quantitative imaging of weekly plankton samples, in the northwestern Mediterranean Sea. We extract 45 morphological features on greater than 800 000 individuals, which we summarize into four main morphological traits (size, transparency, circularity and shape complexity). In this morphological space, we define objective morphological groups and, from those, compute morphological diversity indices (richness, evenness and divergence) using metrics originally defined for functional diversity. On both time scales, morphological diversity increased when nutritive resources and plankton concentrations were low, thus matching the theoretical reciprocal relationship. Over the long term at least, this diversity increase was not fully attributable to taxonomic diversity changes. The decline in the most common plankton forms and the increase in morphological variance and in extreme morphologies suggest a mechanism akin to specialization under low production, with likely consequences for trophic structure and carbon flux.

Beyond the limits of the unassigned protist microbiome: inferring large-scale spatio-temporal patterns of Syndiniales marine parasites.

Marine protists are major components of the oceanic microbiome that remain largely unrepresented in culture collections and genomic reference databases. The exploration of this uncharted protist diversity in oceanic communities relies essentially on studying genetic markers from the environment as taxonomic barcodes. Here we report that across 6 large scale spatio-temporal planktonic surveys, half of the genetic barcodes remain taxonomically unassigned at the genus level, preventing a fine ecological understanding for numerous protist lineages. Among them, parasitic Syndiniales (Dinoflagellata) appear as the least described protist group. We have developed a computational workflow, integrating diverse 18S rDNA gene metabarcoding datasets, in order to infer large-scale ecological patterns at 100% similarity of the genetic marker, overcoming the limitation of taxonomic assignment. From a spatial perspective, we identified 2171 unassigned clusters, i.e., Syndiniales sequences with 100% similarity, exclusively shared between the Tropical/Subtropical Ocean and the Mediterranean Sea among all Syndiniales orders and 25 ubiquitous clusters shared within all the studied marine regions. From a temporal perspective, over 3 time-series, we highlighted 39 unassigned clusters that follow rhythmic patterns of recurrence and are the best indicators of parasite community's variation. These clusters withhold potential as ecosystem change indicators, mirroring their associated host community responses. Our results underline the importance of Syndiniales in structuring planktonic communities through space and time, raising questions regarding host-parasite association specificity and the trophic mode of persistent Syndiniales, while providing an innovative framework for prioritizing unassigned protist taxa for further description.

Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

Machine learning techniques to characterize functional traits of plankton from image data.

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Genomic differentiation of three pico-phytoplankton species in the Mediterranean Sea.

For more than a decade, high-throughput sequencing has transformed the study of marine planktonic communities and has highlighted the extent of protist diversity in these ecosystems. Nevertheless, little is known relative to their genomic diversity at the species-scale as well as their major speciation mechanisms. An increasing number of data obtained from global scale sampling campaigns is becoming publicly available, and we postulate that metagenomic data could contribute to deciphering the processes shaping protist genomic differentiation in the marine realm. As a proof of concept, we developed a findable, accessible, interoperable and reusable (FAIR) pipeline and focused on the Mediterranean Sea to study three a priori abundant protist species: Bathycoccus prasinos, Pelagomonas calceolata and Phaeocystis cordata. We compared the genomic differentiation of each species in light of geographic, environmental and oceanographic distances. We highlighted that isolation-by-environment shapes the genomic differentiation of B. prasinos, whereas P. cordata is impacted by geographic distance (i.e. isolation-by-distance). At present time, the use of metagenomics to accurately estimate the genomic differentiation of protists remains challenging since coverages are lower compared to traditional population surveys. However, our approach sheds light on ecological and evolutionary processes occurring within natural marine populations and paves the way for future protist population metagenomic studies.

Machine Learning for the Study of Plankton and Marine Snow from Images.

Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users.

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.

Identifying copepod functional groups from species functional traits.

We gathered information on the functional traits of the most representative copepod species in the Mediterranean Sea. Our database includes 191 species described by 7 traits encompassing diverse ecological functions: minimal and maximal body length, trophic group, feeding type, spawning strategy, diel vertical migration and vertical habitat. Cluster analysis in the functional trait space revealed that Mediterranean copepods can be separated into groups with distinct ecological roles.