Academic literature on the topic 'Flowcam-Zooscan'

Siliceous Rhizaria (polycystine radiolarians and phaeodarians) are significant contributors to carbon and silicon biogeochemical cycles. Considering their broad taxonomic diversity and their wide size range (from a few micrometres up to several millimetres), a comprehensive evaluation of the entire community to carbon and silicon cycles is challenging. Here, we assess the diversity and contribution of silicified Rhizaria to the global biogenic silica stocks in the upper 500 m of the oligotrophic North-Western Mediterranean Sea using both imaging (FlowCAM, Zooscan and Underwater Vision Profiler) and molecular tools and data. While imaging data (cells m-3) revealed that the most abundant organisms were the smallest, molecular results (number of reads) showed that the largest Rhizaria had the highest relative abundances. While this seems contradictory, relative abundance data obtained with molecular methods appear to be closer to the total biovolume data than to the total abundance data of the organisms. This result reflects a potential link between gene copies number and the volume of a given cell allowing reconciling molecular and imaging data. Using abundance data from imaging methods we estimate that siliceous Rhizaria accounted for up to 6% of the total biogenic silica biomass of the siliceous planktonic community in the upper 500m of the water column.

Abstract Summary In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training data. To compensate the lack of “easy-to-use” software optimized for ecological studies, we propose the EcoTransLearn R-package, which allows greater automation in classification of images acquired with various devices (FlowCam, ZooScan, photographs, etc.), thanks to different TL methods pre-trained on the generic ImageNet dataset. Availability and Implementation EcoTransLearn is an open-source package. It is implemented in R, and calls Python scripts for image classification step (using reticulate and tensorflow libraries). The source code, instruction manual and examples can be found at https://github.com/IFREMER-LERBL/EcoTransLearn. Supplementary information Supplementary data are available at Bioinformatics online.

A major challenge in characterizing plankton communities is the collection, identification and quantification of samples in a time-efficient way. The classical manual microscopy counts are gradually being replaced by high throughput imaging and nucleic acid sequencing. DNA sequencing allows deep taxonomic resolution (including cryptic species) as well as high detection power (detecting rare species), while RNA provides insights on function and potential activity. However, these methods are affected by database limitations, PCR bias, and copy number variability across taxa. Recent developments in high-throughput imaging applied in situ or on collected samples (high-throughput microscopy, Underwater Vision Profiler, FlowCam, ZooScan, etc) has enabled a rapid enumeration of morphologically-distinguished plankton populations, estimates of biovolume/biomass, and provides additional valuable phenotypic information. Although machine learning classifiers generate encouraging results to classify marine plankton images in a time efficient way, there is still a need for large training datasets of manually annotated images. Here we provide workflow examples that couple nucleic acid sequencing with high-throughput imaging for a more complete and robust analysis of microbial communities. We also describe the publicly available and collaborative web application EcoTaxa, which offers tools for the rapid validation of plankton by specialists with the help of automatic recognition algorithms. Finally, we describe how the field is moving with citizen science programs, unmanned autonomous platforms with in situ sensors, and sequencing and digitalization of historical plankton samples.

The Western Tropical South Pacific (WTSP) basin has been identified as a hotspot of atmospheric dinitrogen fixation due to the high dissolved iron ([DFe]) concentrations (up to 66 nM) in the photic layer linked with the release of shallow hydrothermal fluids along the Tonga-Kermadec arc. Yet, the effect of such hydrothermal fluids in structuring the plankton community remains poorly studied. During the TONGA cruise (November-December 2019), we collected micro- (20-200 μm) and meso-plankton (>200 μm) samples in the photic layer (0-200 m) along a west to east zonal transect crossing the Tonga volcanic arc, in particular two volcanoes associated with shallow hydrothermal vents (< 500 m) in the Lau Basin, and both sides of the arc represented by Melanesian waters and the South Pacific Gyre. Samples were analyzed by quantitative imaging (FlowCam and ZooScan) and then coupled with acoustic observations, allowing us to study the potential transfer of phytoplankton blooms to higher planktonic trophic levels. We show that micro- and meso-plankton exhibit high abundances and biomasses in the Lau Basin and, to some extent, in Melanesian waters, suggesting that shallow hydrothermal inputs sustain the planktonic food web, creating productive waters in this otherwise oligotrophic region. In terms of planktonic community structure, we identified major changes with high [DFe] inputs, promoting the development of a low diversity planktonic community dominated by diazotrophic cyanobacteria. Furthermore, in order to quantify the effect of the shallow hydrothermal vents on chlorophyll a concentrations, we used Lagrangian dispersal models. We show that chlorophyll a concentrations were significantly higher inside the Lagrangian plume, which came into contact with the two hydrothermal sites, confirming the profound impact of shallow hydrothermal vents on plankton production.

Le mésozooplancton joue un rôle clé dans les écosystèmes pélagiques méditerranéens, comme intermédiaire entre les niveaux trophiques inférieurs et supérieurs. Dans cette thèse, nous avons recherché la meilleure combinaison de mailles de filets de prélèvement et de techniques d'analyse des échantillons (FlowCAM et ZOOSCAN) pour délivrer la plus complète estimation de l’abondance, la structure de taille et les taux trophiques et métaboliques associés. Cette étude a été réalisée à partir d'échantillons prélevés à deux stations JULIO et ANTARES de Méditerranée nord-ouest.Ensuite, nous avons appliqué cette méthodologie pour étudier la communauté du mésozooplancton (1) en Méditerranée du Nord-Ouest (campagne MOOSE GE 2017), région à productivité printanière et estivale dépendante du processus de convection hivernale, et (2) dans le sud de la Méditerranée (campagne PEACETIME 2017), dont les régions oligotrophes sont épisodiquement fertilisées par des événements de poussières sahariennes.Des analyses multivariées et des diagrammes rang-fréquence, ont montré des différences d’assemblages marquées entre les deux campagnes, mais légères entre les sous-bassins visités. En général, le mésozooplancton total a montré des variations réduites en abondance et biomasse sur l'ensemble de la zone, avec une contribution notable de la fraction des petites tailles, notamment pour les flux associés (demande en carbone, pression de broutage, respiration et excrétion).Nos résultats mettent en évidence que la structure de la communauté du mésozooplancton de Méditerranée est fortement réactive à diverses de forçages externes typiques, dont les événements de dépôts de poussières sahariennes
Mesozooplankton plays a key role in Mediterranean pelagic ecosystems, acting as intermediate link between lower and higher trophic levels. In this thesis, we firstly used samples collected at two time-series stations in the North Western Mediterranean Sea to obtain a large combination of mesh size nets and sample analysis treatments (FlowCAM and ZOOSCAN) allowing to deliver the most complete estimations of its abundance, size structure and trophic and metabolic functions. Then, we applied the optimized methodology to study the mesozooplankton community in two areas: (1) the North Western Mediterranean Sea (during the MOOSE-GE 2017 cruise), a region of which the spring and summer productivity is strongly dependent on the winter deep-water convection process, and (2) the Southern Mediterranean Sea (during PEACETIME cruise 2017), including oligotrophic regions episodically fertilized by Saharan nutrient-rich dust deposition events. The zooplankton taxa assemblages, analyzed using multivariate analysis and rank frequency diagrams, showed marked differences between the two surveys, but slightly differed between sub-basins within each survey. In general, total mesozooplankton showed reduced variations in abundance and biomass values over the whole area, with a noticeable contribution from the small size fraction both for abundance and biomass, and consequently for the zooplankton fluxes (carbon demand, grazing pressure, respiration, and excretion). Our results highlight that the mesozooplankton community structure is strongly reactive to various time-scale external forcings typical of the Mediterranean Sea, including episodic dust deposition events