Effects of turbulence on diatoms of the genus Pseudo-nitzschia spp. and associated bacteria.

Turbulence is one of the least investigated environmental factors impacting the ecophysiology of phytoplankton, both at the community and individual species level. Here, we investigated, for the first time, the effect of a turbulence gradient (Reynolds number, from Reλ = 0 to Reλ = 360) on two species of the marine diatom Pseudo-nitzschia and their associated bacterial communities under laboratory conditions. Cell abundance, domoic acid (DA) production, chain formation, and Chl a content of P. fraudulenta and P. multiseries were higher for intermediate turbulence (Reλ = 160 or 240). DA was detectable only in P. multiseries samples. These observations were supported by transcriptomic analyses results, which suggested the turbulence related induction of the expression of the DA production locus, with a linkage to an increased photosynthetic activity of the total metatranscriptome. This study also highlighted a higher richness of the bacterial community associated with the nontoxic strain of P. fraudulenta in comparison to the toxic strain of P. multiseries. Bacillus was an important genus in P. multiseries cultures (relative abundance 15.5%) and its highest abundances coincided with the highest DA levels. However, associated bacterial communities of both Pseudo-nitzschia species did not show clear patterns relative to turbulence intensity.

On the conditions promoting Pseudo-nitzschia spp. blooms in the eastern English Channel and southern North Sea.

This study investigated the drivers of the blooms of Pseudo-nitzschia seriata and Pseudo-nitzschia delicatissima complexes in the eastern English Channel and southern North Sea. Phytoplankton data series acquired from 1992 to 2020 were analyzed with a multivariate statistical approach based on Hutchinson's niche concept. P. seriata and P. delicatissima complexes were found to be typically present year round, but they bloomed at different periods because they occupied different realized ecological niches. P. delicatissima complex occupied a more marginal niche and was less tolerant than P. seriata complex. P. delicatissima complex typically bloomed in April-May at the same time as Phaeocystis globosa while P. seriata complex blooms were more frequently observed in June during the decline of low intensity P. globosa blooms. P. delicatissima and P. seriata complexes were both favored by low-silicate environments and relatively low turbulence but they responded differently to water temperature, light, ammonium, phosphate and nitrite + nitrate conditions. Niche shifts and biotic interactions played important roles in the control of the blooms of P. delicatissima and P. seriata complexes. The two complexes occupied different sub-niches during their respective low abundance and bloom periods. The phytoplankton community structure and the number of other taxa presenting a niche overlapping the niches of P. delicatissima and P. seriata complexes also differed between these periods. P. globosa was the taxa contributing the most to the dissimilarity in community structure. P. globosa interacted positively with P. delicatissima complex and negatively with P. seriata complex.

An algorithm for the direct estimation of the parameters of the SIR epidemic model from the I(t) dynamics.

The discrete SIR (Susceptible-Infected-Recovered) model is used in many studies to model the evolution of epidemics. Here, we consider one of its dynamics-the exponential decrease in infected cases I(t). By considering only the I(t) dynamics, we extract three parameters: the exponent of the initial exponential increase γ ; the maximum value I max ; and the exponent of the final decrease Γ . From these three parameters, we show mathematically how to extract all relevant parameters of the SIR model. We test this procedure on numerical data and then apply the methodology to real data received from the COVID-19 situation in France. We conclude that, based on the hospitalized data and the ICU (Intensive Care Unit) cases, two exponentials are found, for the initial increase and the decrease in I(t). The parameters found are larger than reported in the literature, and they are associated with a susceptible population which is limited to a sub-sample of the total population. This may be due to the fact that the SIR model cannot be applied to the covid-19 case, due to its strong hypotheses such as mixing of all the population, or also to the fact that the parameters have changed over time, due to the political initiatives such as social distanciation and lockdown.

Characterization of intermittency in zooplankton behaviour in turbulence.

We consider Lagrangian velocity differences of zooplankters swimming in still water and in turbulence. Using cumulants, we quantify the intermittency properties of their motion recorded using three-dimensional particle tracking velocimetry. Copepods swimming in still water display an intermittent behaviour characterized by a high probability of small velocity increments, and by stretched exponential tails. Low values arise from their steady cruising behaviour while heavy tails result from frequent relocation jumps. In turbulence, we show that at short time scales, the intermittency signature of active copepods clearly differs from that of the underlying flow, and reflects the frequent relocation jumps displayed by these small animals. Despite these differences, we show that copepods swimming in still and turbulent flow belong to the same intermittency class that can be modelled by a log-stable model with non-analytical cumulant generating function. Intermittency in swimming behaviour and relocation jumps may enable copepods to display oriented, collective motion under strong hydrodynamic conditions and thus, may contribute to the formation of zooplankton patches in energetic environments.

Scaling Analysis of Ocean Surface Turbulent Heterogeneities from Satellite Remote Sensing: Use of 2D Structure Functions.

Satellite remote sensing observations allow the ocean surface to be sampled synoptically over large spatio-temporal scales. The images provided from visible and thermal infrared satellite observations are widely used in physical, biological, and ecological oceanography. The present work proposes a method to understand the multi-scaling properties of satellite products such as the Chlorophyll-a (Chl-a), and the Sea Surface Temperature (SST), rarely studied. The specific objectives of this study are to show how the small scale heterogeneities of satellite images can be characterised using tools borrowed from the fields of turbulence. For that purpose, we show how the structure function, which is classically used in the frame of scaling time series analysis, can be used also in 2D. The main advantage of this method is that it can be applied to process images which have missing data. Based on both simulated and real images, we demonstrate that coarse-graining (CG) of a gradient modulus transform of the original image does not provide correct scaling exponents. We show, using a fractional Brownian simulation in 2D, that the structure function (SF) can be used with randomly sampled couple of points, and verify that 1 million of couple of points provides enough statistics.

Changes in the swimming behavior of Eurytemora affinis (Copepoda, Calanoida) in response to a sub-lethal exposure to nonylphenols.

Estuarine waters are continuously loaded with chemicals which affect the physiology of aquatic organisms to various extents. They also have adverse effects on a wide range of behaviors. Nonylphenols and related compounds are biodegradation products of the nonionic surfactants nonylphenol polyethoxylates. They are commonly found in the aquatic environment. We observed immediate alterations of the free swimming activity of the calanoid copepod Eurytemora affinis from the Seine estuary in response to a sub-lethal concentration of nonylphenols (4-NP and NP1EC) at environmentally realistic concentrations (2 µg/L). Swimming speed and activity increased for both males and females. The use of copepod kinematic proved to be a sensitive indicator of sub-lethal exposure to pollutants.