Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

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

Community-Level Responses to Iron Availability in Open Ocean Plankton Ecosystems

Predicting responses of plankton to variations in essential nutrients is hampered by limited in situ measurements, a poor understanding of community composition, and the lack of reference gene catalogs for key taxa. Iron is a key driver of plankton dynamics and, therefore, of global biogeochemical cycles and climate. To assess the impact of iron availability on plankton communities, we explored the comprehensive bio-oceanographic and bio-omics data sets from Tara Oceans in the context of the iron products from two state-of-the-art global scale biogeochemical models. We obtained novel information about adaptation and acclimation toward iron in a range of phytoplankton, including picocyanobacteria and diatoms, and identified whole subcommunities covarying with iron. Many of the observed global patterns were recapitulated in the Marquesas archipelago, where frequent plankton blooms are believed to be caused by natural iron fertilization, although they are not captured in large-scale biogeochemical models. This work provides a proof of concept that integrative analyses, spanning from genes to ecosystems and viruses to zooplankton, can disentangle the complexity of plankton communities and can lead to more accurate formulations of resource bioavailability in biogeochemical models, thus improving our understanding of plankton resilience in a changing environment

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Globally consistent quantitative observations of planktonic ecosystems

In this paper we review the technologies available to make globally quantitative observations of particles in general—and plankton in particular—in the world oceans, and for sizes varying from sub-microns to centimeters. Some of these technologies have been available for years while others have only recently emerged. Use of these technologies is critical to improve understanding of the processes that control abundances, distributions and composition of plankton, provide data necessary to constrain and improve ecosystem and biogeochemical models, and forecast changes in marine ecosystems in light of climate change. In this paper we begin by providing the motivation for plankton observations, quantification and diversity qualification on a global scale. We then expand on the state-of-the-art, detailing a variety of relevant and (mostly) mature technologies and measurements, including bulk measurements of plankton, pigment composition, uses of genomic, optical and acoustical methods as well as analysis using particle counters, flow cytometers and quantitative imaging devices. We follow by highlighting the requirements necessary for a plankton observing system, the approach to achieve it and associated challenges. We conclude with ranked action-item recommendations for the next 10 years to move toward our vision of a holistic ocean-wide plankton observing system. Particularly, we suggest to begin with a demonstration project on a GO-SHIP line and/or a long-term observation site and expand from there, ensuring that issues associated with methods, observation tools, data analysis, quality assessment and curation are addressed early in the implementation. Global coordination is key for the success of this vision and will bring new insights on processes associated with nutrient regeneration, ocean production, fisheries and carbon sequestration

Nonlinear effects of body size and optical attenuation on Diel Vertical Migration by zooplankton

International audienceWe adopt a trait-based approach to explain Diel Vertical Migration (DVM) across a diverse assemblage of planktonic copepods, utilizing body size as a master trait. We find a reproducible pattern of body size-dependence of day and night depths occupied, and of DVM. Both the smallest surface-dwelling and the largest deeper-dwelling copepods refrain from migrations, while intermediate-sized individuals show pronounced DVM. This pattern apparently arises as a consequence of size-dependent predation risk. In the size classes of migratory copepods the amplitude of DVM is further modulated by optical attenuation in the ocean water column because increased turbidity decreases encounter rates with visually hunting predators. Long-term changes in the ocean optical environment are expected to alter the vertical distributions of many copepods and thus to affect predator-prey encounters as well as oceanic carbon export

Les communautés planctoniques des bactéries au macroplancton : dynamique temporelle en Mer Ligure et distribution dans l'océan global lors de l'expédition Tara Oceans. - Approche holistique par imagerie -

Le plancton constitue l’essentiel de la biomasse pélagique et est un acteur majeur des cycles biogéochimiques globaux qui régulent le système Terre. Il comprend l'ensemble des organismes portés par les courants, des bactéries aux méduses géantes. La communauté n'est que très rarement étudiée dans son ensemble mais plutôt par fraction. L’expédition Tara Oceans constitue le premier effort de collecte simultané de toutes les classes de taille de plancton à l’échelle de l’océan global. Pour démontrer la faisabilité de cette approche à grande échelle, des échantillons hebdomadaires de plancton, depuis les bactéries jusqu’au macroplancton gélatineux, ont d’abord été analysés en combinant plusieurs instruments d’imagerie sur une période de 10 mois, en un site de référence (point B) dans la rade de Villefranche sur mer. L’imagerie nous a permis de comparer 1) l’information fonctionnelle définie comme l’agrégation de taxons en 18 Groupes Ecologiques de Plancton (GEP), et 2) la structure en taille des communautés échantillonnées sur un intervalle de taille de 6 ordres de grandeur (0.1 µm à 10000 µm). La communauté planctonique au point B évolue en une succession écologique complexe impliquant tous les groupes planctoniques, depuis les bactéries jusqu’aux prédateurs gélatineux du macroplancton. Des évènements impulsifs, tels que des coups de vent, déclenchent des réorganisations de la communauté par un jeu d’interactions entre des contrôles « bottom-up » et « top-down ». Toutefois, le biovolume planctonique total ne varie que d’un seul ordre de grandeur au cours de la période échantillonnée. De même, la structure en taille des communautés planctoniques totales ne varie pas significativement au cours du temps. La stabilité du biovolume total et de la structure en taille suggère que des mécanismes structurant et de compensation forts maintiennent les communautés planctoniques dans un intervalle de biomasse restreint. Le couplage entre données de taille et de taxonomie révèle une réorganisation du réseau trophique entre l’été et l’hiver. En hiver, Le réseau trophique microplancton-zooplancton est dominé par la fonction de broutage. En été, le réseau trophique microplancton-zooplancton est dominé par la fonction de prédation (chaetognathes et gélatineux carnivores). En été, ce réseau trophique s’organise en deux chaines trophiques parallèles et distinctes discriminées par des relations de taille entre proies et prédateurs. Cette réorganisation souligne le rôle clef du zooplancton et de la prédation dans la structuration des communautés planctoniques. Parallèlement à cette analyse temporelle en un point fixe, nous avons montré l’existence de types caractéristiques de communautés zooplanctoniques, associés à des conditions environnementales distinctes, à partir des échantillons de l’expédition Tara Oceans, à l’échelle globale. En utilisant la même méthodologie que pour l’analyse de la dynamique temporelle, nous avons identifié trois types de communautés mésozooplanctoniques à l’échelle globale selon le type d’environement: 1) des communautés associées aux environnements productifs (upwellings côtiers et équatoriaux), 2) des communautés associées aux zones de minimum d’oxygène (OMZs, « Oxygen Minimum Zones »), et 3) des communautés associées aux gyres océaniques oligotrophes. Ce travail constitue une première typologie des communautés zooplanctoniques, structurées en taille et GEP, à l’échelle globale. Il sera complété dans le futur par l’intégration de données issus des autres compartiments planctoniques, et de données d’export vertical de matière organique particulaire pour affiner les estimations des relations qui existent entre phytoplancton, zooplancton et flux biogéochimiques.Plankton constitutes the bulk of pelagic biomass and plays a major role in the global biogeochemical cycles that regulate the earth system. It encompasses all the organisms that drift with the water masses movements, from bacteria to giant medusae. Studies of the entire community are scarce, and plankton has been traditionally studied by fractions. The Tara Oceans expedition is the first attempt to simultaneously collect plankton in every size classes at the global scale. To demonstrate the feasibility of this approach, samples of plankton from bacteria to gelatinous macroplankton were collected weekly over ten months at a reference site (point B), in Villefranche Bay, northwestern Mediterranean, and analyzed using imaging techniques. Imaging enabled us to compare 1) the functional taxonomic information as derived from the analysis of 18 Plankton Ecological Groups (PEGs), and 2) the size structure of the same planktonic community over 6 orders of magnitude in size. The plankton dynamics at point B are driven by a complex succession process involving all plankton groups, from bacteria to macroplanktonic gelatinous predators. Environmental impulsive events such as wind events trigger sharp community level reorganizations via interplay of bottom-up controls followed by top-down controls. However, the total biovolume of the planktonic community varies within only one order of magnitude over the period studied. In addition, the size structure of the entire community does not vary significantly over time. The total biovolume and size structure stability suggest that strong and compensative mechanisms drive community dynamics within a narrow range of biomass variation. The use of both taxonomic and size structured data reveals a reorganization of the food web between winter and summer. In winter and spring the microplanktoniczooplanktonic food web is shaped by the grazing function. In summer, it is shaped by the predation function (chaetognaths and gelatinous predators). In summer, the food web self organizes in two distinct food chains discriminated by size relations between predators and preys. This reorganization underlines the key role of zooplankton and predation in structuring planktonic communities. In parallel to this temporal dynamics study, we used the Tara Oceans expedition samples to study the global scale distribution of mesozooplankton. We showed that characteristic mesozooplanktonic communities were associated with distinct environmental conditions, at the global scale. Using a similar methodology as for the temporal study we found that three different mesozooplanktonic communities were associated with 1) productive environments (e.g. upwellings), 2) Oxygen Minimum Zones, and 3) Oligotrophic oceanic gyres. This work is the first typology of mesozooplanktonic communities at the global scale. It will be further developed in the future by the integration of other planktonic compartments and particulate organic matter fluxes data, to improve our knowledge on the relations between phytoplankton, zooplankton and particulate organic matter fluxes

Biological and environmental time series obtained from Point B, Bay of Villefranche

Ecological succession provides a widely accepted description of seasonal changes in phytoplankton and mesozooplankton assemblages in the natural environment, but concurrent changes in smaller (i.e. microbes) and larger (i.e. macroplankton) organisms are not included in the model because plankton ranging from bacteria to jellies are seldom sampled and analyzed simultaneously. Here we studied, for the first time in the aquatic literature, the succession of marine plankton in the whole-plankton assemblage that spanned 5 orders of magnitude in size from microbes to macroplankton predators (not including fish or fish larvae, for which no consistent data were available). Samples were collected in the northwestern Mediterranean Sea (Bay of Villefranche) weekly during 10 months. Simultaneously collected samples were analyzed by flow cytometry, inverse microscopy, FlowCam, and ZooScan. The whole-plankton assemblage underwent sharp reorganizations that corresponded to bottom-up events of vertical mixing in the water-column, and its development was top-down controlled by large gelatinous filter feeders and predators. Based on the results provided by our novel whole-plankton assemblage approach, we propose a new comprehensive conceptual model of the annual plankton succession (i.e. whole plankton model) characterized by both stepwise stacking of four broad trophic communities from early spring through summer, which is a new concept, and progressive replacement of ecological plankton categories within the different trophic communities, as recognised traditionally