Impact of ocean acidification on phytoplankton growth and aggregation

Since the industrial revolution, the ocean has absorbed around one third of the anthropogenic CO2, which induced a profound alteration of the carbonate system commonly known as ocean acidification. Since the preindustrial times, the average ocean surface water pH has fallen by 0.1 units, from approximately 8.2 to 8.1 and a further decrease of 0.4 pH units is expected for the end of the century. Despite their microscopic size, marine diatoms are bio-geo-chemically a very important group, responsible for the export of massive amount of carbon to deep waters and sediments. The knowledge of the potential effects of ocean acidification on the phytoplankton growth and on biological pump is still at its infancy. This study wants to investigate the effect of ocean acidification on the growth of the diatom Skeletonema marinoi and on its aggregation, using a mechanistic approach. The experiment consisted of two treatments (Present and Future) representing different pCO2 conditions and two sequential experimental phases. During the cell growth phase a culture of S. marinoi was inoculated into transparent bags and the effect of ocean acidification was studied on various growth parameters, including DOC and TEP production. The aggregation phase consisted in the incubation of the cultures into rolling tanks where the sinking of particles through the water column was simulated and aggregation promoted. Since few studies investigated the effect of pH on the growth of S. marinoi and none used pH ranges that are compatible with the OA scenarios, there were no baselines. I have shown here, that OA does not affect the cell growth of S. marinoi, suggesting that the physiology of this species is robust in respect to the changes in the carbonate chemistry expected for the end of the century. Furthermore, according to my results, OA does not affect the aggregation of S. marinoi in a consistent manner, suggesting that this process has a high natural variability but is not influenced by OA in a predictable way. The effect of OA was tested over a variety of factors including the number of aggregates produced, their size and sinking velocity, the algal, bacterial and TEP content. Many of these variables showed significant treatment effects but none of these were consistent between the two experiments.

Ecological modelling of the phytoplankton dynamics in the northern Gulf of Aqaba (Red Sea)

The Gulf of Aqaba represents a small scale, easy to access, regional analogue of larger oceanic oligotrophic systems. In this Gulf, the seasonal cycles of stratification and mixing drives the seasonal phytoplankton dynamics. In summer and fall, when nutrient concentrations are very low, Prochlorococcus and Synechococcus are more abundant in the surface water. This two populations are exposed to phosphate limitation. During winter mixing, when nutrient concentrations are high, Chlorophyceae and Cryptophyceae are dominant but scarce or absent during summer. In this study it was tried to develop a simulation model based on historical data to predict the phytoplankton dynamics in the northern Gulf of Aqaba. The purpose is to understand what forces operate, and how, to determine the phytoplankton dynamics in this Gulf. To make the models data sampled in two different sampling station (Fish Farm Station and Station A) were used. The data of chemical, biological and physical factors, are available from 14th January 2007 to 28th December 2009. The Fish Farm Station point was near a Fish Farm that was operational until 17th June 2008, complete closure date of the Fish Farm, about halfway through the total sampling time. The Station A sampling point is about 13 Km away from the Fish Farm Station. To build the model, the MATLAB software was used (version 7.6.0.324 R2008a), in particular a tool named Simulink. The Fish Farm Station models shows that the Fish Farm activity has altered the nutrient concentrations and as a consequence the normal phytoplankton dynamics. Despite the distance between the two sampling stations, there might be an influence from the Fish Farm activities also in the Station A ecosystem. The models about this sampling station shows that the Fish Farm impact appears to be much lower than the impact in the Fish Farm Station, because the phytoplankton dynamics appears to be driven mainly by the seasonal mixing cycle.

Micropollutants affect the ability of phytoplankton communities to track environmental changes

Human activities strongly influence environmental processes, and while human domination increases, biodiversity progressively declines in ecosystems worldwide. High genetic and phenotypic variability ensures functionality and stability of ecosystem processes through time and increases the resilience and the adaptive capacity of populations and communities, while a reduction in functional diversity leads to a decrease in the ability to respond in a changing environment. Pollution is becoming one of the major threats in aquatic ecosystem, and pharmaceutical and personal care products (PPCPs) in particular are a relatively new group of environmental contaminants suspected to have adverse effects on aquatic organisms. There is still a lake of knowledge on the responses of communities to complex chemical mixtures in the environment. We used an individual-trait-based approach to assess the response of a phytoplankton community in a scenario of combined pollution and environmental change (steady increasing in temperature). We manipulated individual-level trait diversity directly (by filtering out size classes) and indirectly (through exposure to PPCPs mixture), and studied how reduction in trait-diversity affected community structure, production of biomass and the ability of the community to track a changing environment. We found that exposure to PPCPs slows down the ability of the community to respond to an increasing temperature. Our study also highlights how physiological responses (induced by PPCPs exposure) are important for ecosystem processes: although from an ecological point of view experimental communities converged to a similar structure, they were functionally different.