The new benthic toxic dinoflagellate, Ostreopsis fattorussoi sp. nov., is described from the Eastern Mediterranean Sea, Lebanon and Cyprus coasts, and is supported by morphological and molecular data. The plate formula, Po, 3', 7″, 6c, 7s, 5‴, 2'''', is typical for the Ostreopsis genus. It differs from all other Ostreopsis species in that (i) the curved suture between plates 1' and 3' makes them approximately hexagonal, (ii) the 1' plate lies in the left half of the epitheca and is obliquely orientated leading to a characteristic shape of plate 6″. The round thecal pores are bigger than the other two Mediterranean species (O. cf. ovata and O. cf. siamensis). O. fattorussoi is among the smallest species of the genus (DV: 60.07 ± 5.63 μm, AP: 25.66 ± 2.97 μm, W: 39.81 ± 5.05 μm) along with O. ovata. Phylogenetic analyses based on the LSU and internal transcribed spacer rDNA shows that O. fattorussoi belongs to the Atlantic/Mediterranean Ostreopsis spp. clade separated from the other Ostreopsis species. Ostreopsis fattorussoi produces OVTX-a and structural isomers OVTX-d and -e, O. cf. ovata is the only other species of this genus known to produce these toxins. The Lebanese O. fattorussoi did not produce the new palytoxin-like compounds (ovatoxin-i, ovatoxin-j , ovatoxin-j , and ovatoxin-k) that were previously found in O. fattorussoi from Cyprus. The toxin content was in the range of 0.28-0.94 pg · cell . On the Lebanon coast, O. fattorussoi was recorded throughout the year 2015 (temperature range 18°C-31.5°C), with peaks in June and August.
Abstract. A compilation of data from several cruises between 1998 and 2013 was used to derive polynomial fits that estimate total alkalinity (A T ) and total dissolved inorganic carbon (C T ) from measurements of salinity and temperature in the Mediterranean Sea surface waters. The optimal equations were chosen based on the 10-fold cross-validation results and revealed that second-and third-order polynomials fit the A T and C T data respectively. The A T surface fit yielded a root mean square error (RMSE) of ± 10.6 µmol kg −1 , and salinity and temperature contribute to 96 % of the variability. Furthermore, we present the first annual mean C T parameterization for the Mediterranean Sea surface waters with a RMSE of ± 14.3 µmol kg −1 . Excluding the marginal seas of the Adriatic and the Aegean, these equations can be used to estimate A T and C T in case of the lack of measurements. The identified empirical equations were applied on the 0.25 • climatologies of temperature and salinity, available from the World Ocean Atlas 2013. The 7-year averages (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) showed that A T and C T have similar patterns with an increasing eastward gradient. The variability is influenced by the inflow of cold Atlantic waters through the Strait of Gibraltar and by the oligotrophic and thermohaline gradient that characterize the Mediterranean Sea. The summer-winter seasonality was also mapped and showed different patterns for A T and C T . During the winter, the A T and C T concentrations were higher in the western than in the eastern basin. The opposite was observed in the summer where the eastern basin was marked by higher A T and C T concentrations than in winter. The strong evaporation that takes place in this season along with the ultra-oligotrophy of the eastern basin determines the increase of both A T and C T concentrations.
This study presents a method for estimating secondary phytoplankton pigments from satellite ocean color observations. We first compiled a large training data set composed of 12,000 samples; each sample is composed of 10 in situ phytoplankton high‐performance liquid chromatography (HPLC)‐measured pigment concentrations, GlobColour products of chlorophyll‐a concentration, and remote sensing reflectance (Rrs(λ)) data at different wavelengths, in addition to advanced very high resolution radiometer sea surface temperature measurements. The resulting data set regroups a large variety of encountered situations between 1997 and 2014. The nonlinear relationship between the in situ and satellite components was identified using a self‐organizing map, which is a neural network classifier. As a major result, the self‐organizing map enabled reliable estimations of the concentration of chlorophyll‐a and of nine different pigments from satellite observations. A cross‐validation procedure showed that the estimations were robust for all pigments (R2 > 0.75 and an average root‐mean‐square error = 0.016 mg/m3). A consistent association of several phytoplankton pigments indicating phytoplankton group specific dynamic was shown at a global scale. We also showed the uncertainties for the estimation of each pigment.
We present a new method to identify phytoplankton functional types (PFTs) in the Mediterranean Sea from ocean color data (GlobColour data in the present study) and AVHRR sea surface temperature. The principle of the method is constituted by two very fine clustering algorithms, one mapping the relationship between the satellite data and the pigments and the other between the pigments and the PFTs. The clustering algorithms are constituted of two efficient self‐organizing maps, which are neural network classifiers. We were able to identify and estimate the percentage of six PFTs: haptophytes, chlorophytes, cryptophytes, Synechococcus, Prochlorococcus, and diatoms. We found that these PFTs present a peculiar variability due to the complex physical and biogeochemical characteristics of the Mediterranean Sea: Haptophytes and chlorophytes dominate during winter and mainly in the western Mediterranean basin, while Synechococcus and Prochlorococcus dominate during summer. The dominance of diatoms was mainly observed in spring in the Balearic Sea in response to deep water convection phenomena and near the coastline and estuaries due to important continental inputs. Cryptophytes present a weak concentration in the Aegean Sea in autumn. The validation tests performed on in situ matchups showed satisfying results and proved the ability of the method to reconstruct efficiently the spatiotemporal patterns of phytoplankton groups in the Mediterranean Sea. The method can easily be applied to other oceanic regions.
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