The NEREA cruises in spring 2019

Mapping the research cruises

Data from research cruises in the Gulf of Naples (Mediterranean sea), Italy, on four dates in spring 2019 was analysed. Each cruise took a different route and collected a different number of samples. Temperature, salinity and FCM-phytoplankton data were collected on shipboard in "real time". I am 'Zooming in' to the Gulf of Naples, where the NEREA research cruises took place.

The maps below, generated based on shipboard GPS data, show the track, i.e. the sampling stations of the four research cruises.
All research cruises start of the port of Naples. The cruise of the 17th April crossed the Gulf in south-south-westward direction and went offshore, near the island of Capri. The cruises of the 18th April and the 7th May went south-westward along the coast, towards Marechiaro, one of the routine monitoring routes of the SZN. The cruise of the 8th May went south-east, towards the mouth of the river Sarno, from there south-westwards towards Sorrento and then north-westwards towards the center of the Gulf. From there it went offshore in south-westward direction, where the sampling ended.

Environmental Variability

Isosurface temperature maps for the four cruises, colourbars show temperature values in degrees Celsius.
Temperatures are fairly stable between locations and sampling dates, varying only between 15 and 18 degrees, The warmest date is the 08/05 and the sampling stations near the river Sarno recorded the highest temperatures measured throughout the cruises.

Isosurface salinity maps, the colourbars display salinity in parts per thousand (ppt).
The salinity values do not vary greatly, most values lie between 37 & 38 ppt, only near the river Sarno (Map d) a slightly greater variation is observable.

Relative Cell and Carbon contributions by group

The pie charts below show the proportion each group contributes to total cell count and estimated carbon content per sample (see Parameter clarifications, "Total carbon content per group"). Synechococcus spp. is the most abundant group across samples, followed by Nanoeukaryotes and Picoeukaryotes. For the cruise along the coast on the 18th April, the Picocryptophytes have similarly high abundance as the Picoeukaryotes.
In the samples of the 17th April, going further offshore, and the cruise of the 18th April , Nanoeukaryotes have the greatest contribution to the estimated carbon content of samples, followed by the group 2 of Nanocryptophytes and the Pennate-like cells. In the samples of the 7th and 8th of May Nanocryptophytes 2 are the major carbon contributors, with Nanoeukaryotes hvaing the second highest contribution and Microeukaryotes the third highest - despite the instrument and analysis not being calibrated for the microsize range. Interestingly, despite differing ship routes, in terms of estimated carbon contributions, the samples of the 17th and 18th April and the samples of the 7th and 8th of May are more similar to each other than to the ones of the other month, which could be an indication that for the biomass contribution temporal variation is greater than spatial variation. (The 18th April cruise and 7th May cruise took very similar routes).

Relative cell abundances by group

17/04/19

18/04/19

07/05/19

08/05/19

Relative Carbon contribution by group

17/04/19

18/04/19

07/05/19

08/05/19

Spatial variability of Phytoplankton groups

The series of maps below show spatial concentrations of the most abundant groups and groups with the highest biomass contributions in the samples.

Synechococcus spp. Concentration maps - cells/ml
The distribution and concentrations of Synechococcus spp., the group which was most abundant throughout all the analysed samples, are quite variable on both temporal and spatial scales.

Picoeukaryote Concentrations in cells/ml
The concentrations of picoeukaryote cells are low and less variable compared to Synechoccocus cells, but, ranging between about 1000 and 20 000 cells/ml of sampled seawater, there are still one of the more abundant groups in all cruises.

Nanoeukaryote concentrations in cells/ml
Nanoeukaryotes are a diverse group. Despite being present in low densities, they make up a large percentage of the overall biomass and estimated carbon content of the samples. Greatest variations of Nanoeukaryotes occurred between the port, where the Nanoeukaryotes have the highest concentrations of about 20 000 cells/ml seawater and the stations farthest away from the port (concentrations near 0 cells/ml) on the 17th April 2019.

Nanocryptophytes - group 2 concentrations in cells/ml
This group of Nanocryptophytes make up for a large amount of carbon content in the samples, but as visualised here, contribute only a relatively low quantity of cells to the total phytoplankton mixture. Concentrations vary little between locations and typically remain below 10000 cells/ml.

Environmental Correlations

Fans of data tables, your attention, please! Please find below correlation tables of cell traits and concentrations with temperature and salinity, by group. Shown are Pearson's correlations between traits of each group and environmental parameters, for the semi-continuous dataset. Displayed are R-values of the correlations which are significant at an alpha- level of p<0.01, non-significant values are marked by NS. Sign (- or +) and colour indicate the nature of the correlation, positive correlations are displayed in green, negative ones in red.

correlationtables 0705 1704.xlsx

Parameter clarifications

Total Carbon Content of group =Mean carbon content per cell *concentration ml
Mean carbon content per cell=
Coccolithophore-like cells: exp(-0.665+log(0.003*((Mean SWS Total`/54)*11)^1.164))*0.939)
All other groups: exp(-0.665+log(0.0045*(Mean SWS Total`/54)*11)^1.2451))*0.939)

( based on Fragoso et al. 2019)Shape= (Mean FWS Fill factor )-1; The fill factor indicates how solid the created pulse shape is

Length = Mean Forward Scatter Length; The raw length value is estimated from the time of flight between the passing of the 50% of maximum threshold. A subsequent correction is applied through the use of a correction curve. The determination of the correction curve follows physical considerations and measurements (Jerico Next 2017).

BV = Bio Volume = (((Mean SWS Total/54)x11 )^1.2451)x 0.0045for all Sets except the Coccolithophore-like cells. Since these have an extraproportional sideward scatter, in that case the forward scatter was used to calculate BV, resulting in the following equation: BV=0.003x(Mean FWS Total^1.164)

The estimation of Bio Volume was based on Fragoso et al. 2019, which takes a spherical shape as a base for the calculations.In my Rcode it looks as follows: (Mean SWS Total/54)x11 # This step normalization to the PMT level of the calibration experiment.mean_bvnoncocco<-0.0045*(traits$`Mean SWS Total`[which(traits$Set != "Cocco-like")]^1.2451) # relationship of the calibration experiment; in µm3mean_bvcocco<-0.003*(traits$`Mean FWS Total`[which(traits$Set =="Cocco-like")]^1.164)

Chl/BV = Chlorophyll a over Bio Volume= Mean Fluorescense Red Total /Bio VolumeThe mean red fluorescence is indicative of the chlorophyll a content, since chlorophyll a is the primary photosynthetic pigment which induces red fluorescence.

Phyco/Chl/BV = Phycoerythrin to Chlorophyll a to Bio Volume ratio.This is estimated by dividing Mean Orange Fluorescence (as estimate of the pigment Phycoerythrin) by Mean Red Fluorescence (as estimate of chlorophyll a content) over Bio Volume (Cell size estimate), i.e. Mean FLO/ Mean FLR/ BV. This indicator shows how the ratio of orange to red fluorescence (or Phycoerythrin to Chla) changes with cell size.

Asymmetry = Measures the asymmetry of the pulseshape and thus provides an indication of the distribution of the signal over the length of the particle, i.e. the eveness of the signal.

Structural Complexity = Mean SWS total / Mean FWS total; The SWS signal is an estimator of the complexity of the cell, the more organelles & structures a cell contains, the more light is scattered side wards, i.e. the higher the SWS signal. The FWS signal is an indicator of the size of the cell, the bigger the cell, the more light is scattered in forward direction when it passes through the laser beam. The ratio of SWS to FWS gives thus an indication of the complexity of the cell structure.

Key Findings

Temperatures varied between 15 and 18 degrees across samples, Salinity between 35 and 38ppt, with the greatest spatial variations occuring in the cruise of the 7th May, going near the mouth of the Sarno river.
Throughout the four cruises, Synechococcus spp. is the most abundant functional group
Synechococcus spp. is also the group with the most spatial - temporal variation
Nanoeukaryotes and the second group of Nanocryptophytes have the greates overall contribution to the carbon contents of the samples
Cell concentrations and BV of Synechococcus are positively correlated with temperature, and Synechococcus cell concentrations only are negatively correlated with salinity, concentrations of all other groups are negatively correlated with temperature, Nanocryptophytes 1 and Microeukaryote cell concentrations are positively correlated with salinity
Structural complexity of Synechococcus spp., Nanoeukaryotes, Picocryptophytes, Microeukaryutes and Pennate-like cells is positively correlated with temperature, Structural complexity of Synechococcus spp. and Microeukaryotes are negatively correlated with Salinity and Structural complexity of Picoeukaryotes is negatively correlated with temperature
Length of both Picoeukaryote groups, Nanoeukaryotes and Coccolithophore-like cells is negatively correlated with temperature, length of Picocryptophytes is negatively correlated with salinity and length of Pennate-like cells is positively correlated with salinity
Shape of Synechoccocus and Nanocryptophytes 1 are negatively related with Salinity, Nanoeukaryote and Pennate shapes are positively correlated with salinity, Shape of Picoeukaryotes, Nanocryptophytes 1 and Picocryptophytes are positively correlated with temperature, Pennate shape is negatively correlated with temperature.

Conclusions

Scanning Flow Cytometry is an efficient method to collect a large amount of data on phytoplankton diversity, distribution and cell traits. It has a very wide range of applications and ongoing technological developments increase the quality and efficiency of the data collection. The analysis of these large generated datasets remains somewhat challenging, and if manual clustering methods are applied, subjective. The rise of new automated clustering approaches is promising, and could make the data analysis more objective and unified across the research community. Potentially automated analysing approaches could even facilitate the wider application of SCFM through significantly decreasing the workload.

The application of SFCM ultraplankton data analysis on four cruises in the Gulf of Naples has unvealed some of the diversity of the Phytoplankton communities in this basin. The analysis showed that picoplanktonic organims (Synechoccoccus spp. and Picoeukaryotes) are the most abundant specimen in the phytoplankton community but other groups in the nano-size range such as Nanoeukaryotes and certain Nanocryptophytes make the greatest contribution to the phytoplankton biomass. Absolute concentrations and cell parametres vary on spatial and temporal scales. Salinity and temperature can explain some of this variability but to determine the drivers of phytoplankton diversity and distribution in the Gulf of Naples, more environmental parameters need to be evaluated and more samples to be collected and analysed.
That is exactly what is being done at the SZN :)

Personal remarks

Despite not being able to visit the research lab and being over two months locked down in Italy - I learned a lot during my Professional Practice. I had no idea about picoplankton diversity or flowcytometry beforehand and now I am here, sharing this knowledge with you. The work from home was very exhausting and sometimes frustrating, but what is better than the moment in which your graphs in R eventually look the way they are supposed to? - Ah, there is one thing: after being locked in a small flat for over a month, getting out and jumping head first into the cold sea!

References

Fragoso, G.M., Poulton, A.J., Pratt, N.J., Johnsen, G. and Purdie, D.A., 2019. Trait‐based analysis of subpolar North Atlantic phytoplankton and plastidic ciliate communities using automated flow cytometer. Limnology and Oceanography, 64(4), pp.1763-1778

JERICO-NEXT-WP3-D3.1, 4 Oct. Sep 2017

R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

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