Vincent Vantrepotte

GROWTH INHIBITION AND TOXICITY OF THE DIATOM ALDEHYDE 2‐TRANS, 4‐TRANS‐DECADIENAL ON THALASSIOSIRA WEISSFLOGII (BACILLARIOPHYCEAE)

A common aldehyde present in marine and freshwater diatoms, 2‐trans, 4‐trans‐decadienal (A3), is involved in the wound‐activated response of diatoms to copepod grazing. Upon breakage of the diatom cell membrane, aldehydes are enzymatically produced by the rapid conversion of precursors and strongly impact copepod reproduction by impairing egg production and hatching success, inducing teratogenic embryos modifications. In this study, A3 was assayed with the marine diatom Thalassiosira weissflogii (Grunow) Fryxell et Hasle. The aldehyde concentration necessary to reduce 50% growth rate (EC50) was 0.29 mg·L−1. Decadienal was found to inhibit T. weissflogii cell growth in a dose‐ and time‐dependent manner, with irreversible effects after 24 h of exposure. Decadienal induced a degenerative process, through modifications of cell membrane characteristics, interference with cell cycle progression, and with cell metabolic activity, leading to cell death. A preferential action of A3 on dividing cells was observed. Photosynthetic efficiency significantly decreased upon exposure to the aldehyde, paralleled by an increase in diatoxanthin, suggesting a protective role of this xanthophyll, usually involved in photoprotection. Dying cells exhibited the morphological and biochemical features that bear close resemblance to apoptosis of mammalian cells, including cell shrinkage, chromatin condensation, and degradation of nuclear DNA to nucleosomal size fragments. These data are the first direct evidence to show aldehydes are toxic to diatoms. We suggest a possible nontoxic role of such compounds as chemical signals of unfavorable conditions within the phytoplankton communities, which may be relevant for the population dynamics of diatoms during blooms.

Effect of inherent optical properties variability on the chlorophyll retrieval from ocean color remote sensing: an in situ approach

The impact of the inherent optical properties (IOP) variability on the chlorophyll, Chl, retrieval from ocean color remote sensing algorithms is analyzed from an in situ data set covering a large dynamic range. The effect of the variability of the specific phytoplankton absorption coefficient, a(phy)/Chl, specific particulate backscattering coefficient, b(bp)/Chl, and colored detrital matter absorption to non-water absorption ratio, a(cdm)/a(nw), on the performance of standard operational algorithms is examined. This study confirms that empirical algorithms are highly dependent on the specifics IOP values (especially b(bp)/Chl and a(cdm)/a(nw)): Chl is over-estimated in waters with specific IOP values higher than averaged values, and vice versa. These results clearly indicate the necessity to account for the influence of the specific IOP variability in Chl retrieval algorithms.

Development of a Semi-Analytical Algorithm for the Retrieval of Suspended Particulate Matter from Remote Sensing over Clear to Very Turbid Waters

Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, Rrs(λ), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g·m−3. Different empirical and semi-analytical approaches developed to assess SPM from Rrs(λ) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.

CDOM-DOC relationship in contrasted coastal waters: implication for DOC retrieval from ocean color remote sensing observation.

Increasing our knowledge on dissolved organic carbon (DOC) spatio-temporal distribution in the coastal ocean represents a crucial challenge for better understanding the role of these ecosystems in the global oceanic carbon cycle. The assessment of DOC concentration from the absorption properties of the colored part of the dissolved organic matter (a(cdom)) was investigated from an extensive data set covering a variety of coastal environments. Our results confirmed that variation in the a(cdom)(412) to DOC ratio (a*(cdom)(412)) can be depicted from the CDOM spectral slope in the UV domain (S(275-295)). They also evidenced that regional first order variation in both a*(cdom)(412) and S(275-295) are highly correlated to variation in a(cdom)(412). From these observations, generalized relationships for estimating a*(cdom)(412) from S(275-295) or a(cdom)(412) were parameterized from our development sites (N = 158; English Channel, French Guiana, Hai Phong Bay) and tested against an independent data set covering others coastal regions (N = 223; French Polynesia, Rhone River estuary, Gulf of Maine, Chesapeake Bay, Southern Middle Atlantic Bight) demonstrating the possibility to derive DOC estimates from in situ CDOM optical properties with an average accuracy of ~16% over very contrasted coastal environments (with DOC ranging from 50 to 250 µmol.L(-1)). The applicability of these generalized approaches was evaluated in the context of ocean color remote sensing observation emphasizing the limits of S(275-295)-based formulations and the potential for a(cdom)-based approaches to represent a compelling alternative for assessing synoptic DOC distribution.

Assessment of the colored dissolved organic matter in coastal waters from ocean color remote sensing.

Knowledge on absorption by colored dissolved organic matter, a(cdom), spatio-temporal variability in coastal areas is of fundamental importance in many field of researches related to biogeochemical cycles studies, coastal areas management, as well as land and water interactions in the coastal domain. A new method, based on the theoretical link between the vertical attenuation coefficient, K(d), and the absorption coefficient, has been developed to assess a(cdom). This method, confirmed from radiative transfer simulations and in situ measurements, and tested on an independent in situ data set (N = 126), allows a(cdom) to be assessed with a Mean Relative Absolute Difference, MRAD, of 33% over two order of magnitude (from 0.01 to 1.16 m(-1)). In the frame of ocean color observation, K(d) is not directly measured but estimated from the remote sensing reflectance, R(rs). Based on 109 satellite (SeaWiFS) and in situ coincident (i.e. match-up) data points a(cdom) is retrieved with a MRAD value of 37%. This simple model generally presents slightly better performances than recently developed empirical or semi-analytical algorithms.

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Knowing that coastal areas concentrate about 60% of the worlds population (within 100 km from the coast), that 75-90% of the global sink of suspended river load takes place in coastal waters in which about 15% of the primary production occurs, the ecological, societal and economical value of these areas are obvious (fish resources, aquaculture, water quality information, recreation areas management, global carbon budget, etc). In that context, precise assessment of suspended particulate matter (SPM) concentrations and of the phenomena controlling its temporal variability is a key objective for many research fields in coastal areas. SPM which encompasses organic (living and non-living) and inorganic matter controls the penetration of light into the water and brings new nutrients into the system, both key parameters influencing phytoplankton primary production. Concentrations and availability of SPM are also known to control rates of food intake, growth and reproduction for various filter feeder organisms. Phytoplankton is highly sensitive to environmental perturbations (such as nutrient inputs, light, and turbulence). The abundance, biomass and dynamics of phytoplankton in coastal areas therefore reflect the prevailing environmental conditions and represent key parameters for assessing information on the ecological conditions, as well as on the coastal water quality. Because phytoplankton is highly sensitive to environmental perturbations [1], its distribution patterns and temporal variability represent good indicators of the ecological conditions of a defined region [2, 3]. Coastal waters also host complex ecosystems and represent important fishery areas that support industry and provide livelihood to coastal settlements. The food chain in the coastal ocean is generally short (especially in upwelling systems, having as low as three trophic levels) whereas the open ocean food web presents up to six trophic levels [4]. As a result, when compared to the open ocean, a relative lower fraction of the primary production gets respired in the coastal ocean while a higher fraction reaches the uppermost trophic level (fish) [5] or is exported to adjacent areas (coastal or open sea)...

Dispersal and Diving Adjustments of the Green Turtle Chelonia mydas in Response to Dynamic Environmental Conditions during Post-Nesting Migration

In response to seasonality and spatial segregation of resources, sea turtles undertake long journeys between their nesting sites and foraging grounds. While satellite tracking has made it possible to outline their migration routes, we still have little knowledge of how they select their foraging grounds and adapt their migration to dynamic environmental conditions. Here, we analyzed the trajectories and diving behavior of 19 adult green turtles (Chelonia mydas) during their post-nesting migration from French Guiana and Suriname to their foraging grounds off the coast of Brazil. First Passage Time analysis was used to identify foraging areas located off Ceará state of Brazil, where the associated habitat corresponds to favorable conditions for seagrass growth, i.e. clear and shallow waters. The dispersal and diving patterns of the turtles revealed several behavioral adaptations to the strong hydrodynamic processes induced by both the North Brazil current and the Amazon River plume. All green turtles migrated south-eastward after the nesting season, confirming that they coped with the strong counter North Brazil current by using a tight corridor close to the shore. The time spent within the Amazon plume also altered the location of their feeding habitats as the longer individuals stayed within the plume, the sooner they initiated foraging. The green turtles performed deeper and shorter dives while crossing the mouth of the Amazon, a strategy which would help turtles avoid the most turbulent upper surface layers of the plume. These adjustments reveal the remarkable plasticity of this green turtle population when reducing energy costs induced by migration.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohens κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012–2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.

Evaluation of the MODIS-Aqua Sea-Surface Temperature product in the inner and mid-shelves of southwest Buenos Aires Province, Argentina

Validation of sea-surface temperature (SST) provided by the MODIS-Aqua sensor (Moderate Resolution Imaging Spectroradiometer) for the inner and mid-shelves of the southwest of Buenos Aires Province (Argentina), is presented for the first time. In situ data obtained with a multi-parametric sonde YSI-6600 and a CTD SBE91 between 2002 and 2011 are used for comparison with the satellite SST product. The match-up exercise was established after comparing different spatial boxes, time difference windows, wind speeds, and also a coefficient of variation. The comparison exercise was made in the coastal zone and the rest of the inner and mid-shelves separately. In the coastal zone, applying a 3 × 2 pixel box and a time window of ±3 hours led to the most accurate results, with a coefficient of determination (R2) of 0.99, a bias of 0.62°C, and a root-mean-square-error (RMSE) of 0.79°C. In the inner-mid-shelves when applying a coefficient of variability <0.3, a time window of ±3 hours, and taking only values of wind speed > 6 m s−1, R2 is 0.97, bias is 0.46°C, and RMSE is 0.95°C. Wind speed plays a major role in the inner-mid-shelves as the SST product is affected by stratification and formation of a diurnal thermocline in the ‘skin and sub-skin layer’ when wind speed is below 6 m s−1. The results for the two shelves are very similar. Finally, the spatial and temporal variability of the SST satellite product was analysed in the study area for the period August 2002–December 2010. The results show that inter-annual variability is not significant and that there is no positive or negative trend for the 9 years of the study. Seasonality is the main component of temporal variability, with variation in amplitude signal depending on bathymetry changes, physical forcing, stability of the water column, and presence of flood plains.

Using CDOM optical properties for estimating DOC concentrations and pCO 2 in the Lower Amazon River

Coloured dissolved organic matter (CDOM) is one of the major contributors to the absorption budget of most freshwaters and can be used as a proxy to assess non-optical carbon fractions such as dissolved organic carbon (DOC) and the partial pressure of carbon dioxide (pCO2). Nevertheless, riverine studies that explore the former relationships are still relatively scarce, especially within tropical regions. Here we document the spatial-seasonal variability of CDOM, DOC and pCO2, and assess the potential of CDOM absorption coefficient (aCDOM(412)) for estimating DOC concentration and pCO2 along the Lower Amazon River. Our results revealed differences in the dissolved organic matter (DOM) quality between clearwater (CW) tributaries and the Amazon River mainstream. A linear relationship between DOC and CDOM was observed when tributaries and mainstream are evaluated separately (Amazon River: N = 42, R2 = 0.74, p<0.05; CW: N = 13, R2 = 0.57, p<0.05). However, this linear relationship was not observed during periods of higher rainfall and river discharge, requiring a specific model for these time periods to be developed (N = 25, R2 = 0.58, p<0.05). A strong linear positive relation was found between aCDOM(412) and pCO2(N = 69, R2 = 0.65, p<0.05) along the lower river. pCO2 was less affected by the optical difference between tributaries and mainstream waters or by the discharge conditions when compared to CDOM to DOC relationships. Including the river water temperature in the model improves our ability to estimate pCO2 (N = 69; R2 = 0.80, p<0.05). The ability to assess both DOC and pCO2 from CDOM optical properties opens further perspectives on the use of ocean colour remote sensing data for monitoring carbon dynamics in large running water systems worldwide.