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Sampling sites and environmental variables
TARA Oceans took place between 2009 and 2013. Among the 210 stations that were sampled, 57 stations covering 7 ocean basins (Fig. 1) were sampled with a Multinet (Stephane Pesant et al., 2015; Roullier et al., 2014a), a sampling device with five nets that allows for depth-stratified sampling (see below).
A CTD rosette equipped with optical sensors was deployed to measure the physico-chemical parameters within the water column. Temperature and conductivity were measured from the surface to a maximum of 1300 m depth using a Seabird 911 CTD mounted on a Sea-Bird Carousel sampler with 10 Niskin bottles. The following additional sensors were mounted to measure optical properties related to relevant biogeochemical variables: fluorometer (Wetlab ECO-AFL/FL model), dissolved oxygen sensor (model SBE 43), nitrate sensor (ISUS with a maximum rating depth of 1000m Satlantic SA), a 25 cm transmissometer for particles 0.5–20 μm (WETLabs), a one-wavelength backscatter meter for particles 0.5–10 μm (WETLabs), and an Underwater Vision Profiler 5 (UVP5) for particles >150 μm and zooplankton >600 μm (Hydroptic). Assuming that particle sinking speed increases with size, those particles detected through the backscattering will be referred to as suspended particulate matter (SPM, particles
< 10µm in Equivalent Spherical Diameter), while the ones detected by the UVP5 (>150µm in Equivalent Spherical Diameter) will be referred as particles. Vertical particle mass flux (in mg Dry Weight m-2d-1)) was calculated from the particle size spectra detected by the UVP5 as in (Guidi et al., 2008b). Based on the High Pressure Liquid Chromatography (HPLC) analysis of water collected with Niskin bottles, we used the method of Uitz et al. (2006) to estimate.
Zooplankton sampling, digitization, biomass estimates
A Hydrobios Multinet (with a 300µm mesh and an aperture of 0.25m2) was used to sample zooplankton (Roullier et al., 2014; Pesant et al., 2015) in five distinct water layers ranging from the surface to occasionally 1300 m depth. The five depth layers were locally defined as a function of the vertical structure of the water column according to the profiles of temperature, salinity, fluorescence, nutrients, oxygen, and particulate matter (Pesant et al., 2015). The Multinet was equipped with a flowmeter to measure the volume of seawater filtered by each net tow (Pesant et al., 2015). Day and night net tows were conducted at ten stations. Sampling at the other stations occurred at day or night, depending on cruise schedule and operational constraints. Once collected, the samples were preserved in a solution of buffered formaldehyde-Borax solution (4%). In the laboratory, the samples were rinsed and split with a Motoda box (MOTODA, 1959). The final split was analysed with the Zooscan imaging system (Gorsky et al., 2010) which allowed a rapid and semi-automatic analysis of zooplankton samples. In total, the samples comprised nearly 400,000 images of living zooplankton and detritus. These images were imported into Ecotaxa, an online platform which allows an automatic prediction of the taxonomic classification of each single image followed by a manual validation/correction. The organisms were then identified manually down to the order, sometimes to the family and rarely down to the genus level. All copepods were sorted at the family level apart from the smallest copepods that cannot be recognised at the family level from the image. They were all grouped into one category called Other-copepoda or other-Calanoida if their morphological features allowed classifying them as Calanoida. This initial sorting allowed classifying zooplankton into 119 taxa. As many taxa showed a very small contribution to total zooplankton abundance, the 119 taxa were grouped into 19 taxonomic groups (Table 1). Those include all the major zooplankton groups that are frequently observed in the oceans. To investigate vertical patterns in mesozooplankton abundance, these 19 groups were further aggregated into eight groups representing a combination of taxonomic and functional classification (Table 1).
Analyzing zooplankton and particles vertical distributions
Vertical attenuation rates of zooplankton (abundance and biomass) and estimated particle fluxes were estimated, from the five sampled layers for zooplankton and from the 5 meter resolution profile of estimated vertical flux using a linear regression of the log-log (i.e. natural logarithm) with the following equation : = ( / ) (3) 0 0.
where represents the zooplankton abundance, the zooplankton biomass or the particle vertical flux at the depth level , 0the zooplankton biomass or abundance and vertical particle flux at the depth 0 (chosen as median depth of the surface net) and the slope taken as a proxy of the attenuation rate of zooplankton biomass zooplankton abundance or particle flux. In the rest of the manuscript, A_zoo represents the slope b of vertical profile for zooplankton abundance, B_zoo the slope b of vertical profile for zooplankton biomass, A_flux the slope b of vertical profile for particle flux, and P_flux1, P_flux2 and P_flux3 the particle flux in respectively the epipelagic, upper and lower mesopelagic. To analyse latitudinal patterns in attenuation rates, the slope values were separated into three latitudinal bands based on the latitudinal position of their corresponding sampling stations: intertropical (0°-30°), temperate (30-60°) and polar (60°-90°). The intertropical stations gathered both OMZ and non-OMZ stations, which allowed us to analyse the effect of oxygen depletion on zooplankton and particles. Non-parametric variance analyses (Kruskal and Wallis tests) were performed to test for differences in slope values (i.e. zooplankton and particles attenuation rates) between latitudinal bands.
Multivariate analysis of community composition
To explore the response of zooplankton community composition to environmental drivers across depth layers, the non-interpolated abundances of the 19 taxonomic groups mentioned above were aggregated into three layers: the epipelagic layer (0-200m), the upper mesopelagic layer (200-500m) and the lower mesopelagic layer (500-1000m). To analyse separately the three depth layers, the samples collected in overlapping layers (18.59% of the total samples) were not included in the statistical analysis (Table S1). To characterise the environmental conditions of each layer at each sampling station the median values of the following contextual environmental variables were used: temperature (T), salinity (S), oxygen (O2), nitrate concentration (NO3), chlorophyll a concentration (Chl_a), phytoplankton size fractions ( _ , _ , and _ ), concentration of suspended particles (SPM) and particle lux (P_Flux). The measurements of all these environmental variables are available on PANGAEA (https://doi.org/10.1594/PANGAEA.840721).
To estimate the strength of the Diel Vertical Migration (DVM) at 10 stations, pairwise Wilcoxon tests were performed to compare in each layers the abundance and biomass of each taxa between day and night. For those 10 same pairs of stations, we used an analysis of similarities (ANOSIM) to test for significant variations in community composition between day and night samples. The ANOSIM tested whether the inter-groups difference (day and night groups) was higher than the intra-groups difference, by providing an R coefficient. An R coefficient close to one suggests dissimilarity between groups, while R value close to zero suggests an even distribution of high and low ranks within and between groups. An R value below zero suggests that dissimilarities are greater within groups than between groups (Clarke and Gorley, 2001). ANOSIM tests were performed within each layer using log-transformed (where log is the natural logarithm) abundances and Bray-Curtis distance among stations.
For each depth layer, a canonical redundancy analysis (RDA) was performed based on the abundances of the 19 mesozooplankton groups and the above-mentioned environmental variables to explore the explanatory power of these variables in structuring the mesozooplankton community. The RDA is an extension of the multiple regression analysis applied to multivariate data (P. Legendre and Legendre, 1998). It allows representing the response variables (abundances of the 19 mesozooplankton groups) in a “constrained” reduced space, i.e., constructed from the explanatory variables (the environmental variables). For each RDA, the following variables were used as “supplementary variables” of the analysis in order to visualize their correlation with the environmental structuring of the mesozooplankton community (i.e., to visualise their position in the RDA space): attenuation of particle flux (A_flux), attenuation of zooplankton abundance (A_zoo), attenuation of zooplankton biomass (B_zoo) and the Shannon index (H’). Beforehand, a Hellinger transformation was performed on the mesozooplankton abundances. A preliminary RDA based on all samples together showed a very strong effect of depth on mesozooplankton community composition (Fig. S1). Therefore, to avoid such a strong effect of depth on the community composition analysis, three separate RDAs were performed on each of the three layers defined above. Significant axes were identified using the Kaiser-Guttman criterion (Legendre and Legendre, 1998).
Data manipulation and statistical analyses were performed with Matlab 2018b (MATLAB 9.5) for the vertical profiles of abundance and biomass and statistical test (Wilcoxon test, Kruskal-wallis test), R environment v3.5.1 (using the following packages: vegan version 2.5-5, ggplot2 version 3.1.1, ggrepel version 0.8.0 and ggfortify version 0.4.7) for the redundancy analysis and PRIMER6 (Version 6.1.12) and PERMANOVA+ (Version 1.0.2) for the ANOSIM test.
Vertical patterns in zooplankton total abundance and biomass and particle flux
On a global scale, zooplankton abundance and biomass decreased exponentially with depth (Fig. 2) in the different latitudinal bins. Abundance and biomass decrease rate with depth and were correlated (r2=0.342, p=4.6 10-5) but the biomass attenuation rate estimates were systematically lower than the attenuation based on the abundance profiles. On average, polar waters showed increased zooplankton abundance and biomass compared to the stations located in the tropics. In the epipelagic layer, abundances and biomass ranged from 1 to 5000 ind m-3 and 0.05 to 200 mg C m-3 while in the mesopelagic they were reduced to 0.05 to 450 ind m-3 and 0.005 to 40 mg C m-3. Copepods were the most abundant being 85% and 65% of the abundance and biomass in the epipelagic, 85 and 76% in the upper mesopelagic and 95% and 97% in the lower pelagic (Table 3). The estimated vertical flux also decreased with depth in all latitudinal bands. On average polar waters showed higher fluxes compared to the stations located in the tropics.
Structuring of the epipelagic community composition.
In the epipelagic layer (0 – 200 m depth), the environmental variables explained 32.71% of the total variance in mesozooplankton groups’ abundances. The first RDA axis (RDA1, 57.18 % of constrained variance) opposed the samples from polar waters, and especially those from the Arctic dominated by Calanidae and crustacean larvae (RDA1 > 0), to the tropical samples presenting more even contributions from most of the remaining groups: Protista, Eumalacostraca, Annelida, Amphipoda, Corycaeidae, Chaetognatha, Euphausiacea, Oithonidae, Ostracoda, Oncaeidae, Calanoida (RDA1 < 0). RDA1 was negatively scored by temperature and salinity and positively scored by vertical particle flux, microphytoplankton contribution, suspended particle concentration, dissolved oxygen concentration and chlorophyll a concentration. Among the supplementary variables, the attenuations of the particle flux and of the zooplankton biomass were positively correlated with RDA1, while the attenuation of the zooplankton abundance and the Shannon index were negatively correlated to RDA1. RDA2 (13.1% of constrained variance) opposed the samples from the Indian Ocean and North Atlantic Ocean that present higher abundances of Cnidaria, Mollusca, Tunicata and Cladocera (RDA2 < 0) to those samples from the Southern Ocean presenting higher abundances of Annelida, Euphausiacae, Amphipoda and Other Copepoda (RDA2 > 0). RDA2 was positively scored by nitrate concentrations and the relative contribution of nanophytoplankton. It was negatively scored by the concentration of suspended particles and the relative contribution of microphytoplankton. All supplementary variables were negatively correlated with RDA2.
Table of contents :
Chapitre I: Introduction générale
I.1. Le plancton et le cycle du carbone dans l’océan
I.2. La zone mésopélagique:
I.3. Plancton et changement climatique
I.4. Les zones à minimum d’oxygène
I.5 La structure en taille du plancton marin
I.6 Taux physiologiques
I.7. Objectifs de cette thèse
References
Chapitre II. Patterns of mesozooplankton community composition and vertical fluxes in the global ocean
II.1. Résumé
II.2. Article soumis et accepté
Abstract
Introduction
Materials and methods
Results
Discussion
Conclusions
ACKNOWLEDGMENTS
References
Supplementary material
II.3. Comparaison des communautés entre les biogéographies spatiales existantes dans l’océan
global
Introduction
Méthode
Résultat-Discussion
Meilleur prédicteur de la distribution spatiale du zooplancton
Chapitre III.1: Diversity of zooplankton NBSS in the global ocean
III.1.1. Résumé
III.1.2. Article en préparation
Abstract
Introduction
Material and methods
Results
Discussion
Conclusion
References
Supporting Material
Chapitre III.2: Contribution of plankton to the Normalized Biovolume Size
Distribution in the upper 1000m of open ocean
III.2.1. Résumé
III.2.2. Article en préparation
Abstract
Introduction
Materials and methods
Results
Discussion
References
Acknowledgement
Supplementary
Chapitre IV : Distribution en taille, budget de la respiration et de l’excrétion des crustacés dans la zone inter-tropicale de l’océan mondial : TARA Océans- GEOMAR-MALASPINA
IV.1. Introduction
IV.2. Matériels et Méthodes
IV.2.1. Les données d’hydrologie, d’optique, d’imagerie et satellites
IV.2.2. Echantillons du Zooplancton et digitalisation
IV.2.3. Identification des images
IV.2.4. Construction du spectre et distribution en taille du plancton
IV.2.5. Analyse statistique
IV.2.6. Estimation des Biomasses et des taux de respiration et d’excrétion
IV.3. Résultats
IV.3.1. Variabilité des NBSS
IV.3.2. Sources de variance
IV.3.3. Pentes spectrales, tailles et biovolumes des crustacés
IV.3.4. Respiration et excrétion des crustacés
IV.4. Discussion
IV.4.1. Qualité des données et les limites de notre travail
IV.4.2. Les sources de variance dans la distribution spatiale des crustacés
IV.4.3. Les grands types de NBSS des crustacés
IV.4.4. Biomasse, Respiration, excrétion
IV.4.5. Contribution de la respiration des crustacés au Budget de carbone.
IV.5. Conclusion
IV.6. Références
Chapitre V: Conclusion générale et perspectives
V.1. Résumé des résultats principaux
V.1.1. Structure verticale de la communauté du mésozooplancton dans l’océan mondial
V.1.2. Distribution 3D des NBSS du zooplancton
V.1.3. Etudes comparatives des NBSS collectés et in situ
V.1.4. Budget carbone des crustacés dans les zones inter-tropicales
VI.2. Perspectives générales
Réferences:
