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.

Modelling the dynamics of phytoplankton with the needs of the end user in mind

How to regulate phytoplankton growth in water supply reservoirs has continued to occupy managers and strategists for some fifty years or so, now, and mathematical models have always featured in their design and operational constraints. In recent years, rather more sophisticated simulation models have begun to be available and these, ideally, purport to provide the manager with improved forecasting of plankton blooms, the likely species and the sort of decision support that might permit management choices to be selected with increased confidence. This account describes the adaptation and application of one such model, PROTECH (Phytoplankton RespOnses To Environmental CHange) to the problems of plankton growth in reservoirs. This article supposes no background knowledge of the main algal types; neither does it attempt to catalogue the problems that their abundance may cause in lakes and reservoirs.

Modelling a light-driven phytoplankton succession

We used a numerical model to investigate if and to what extent cellular photoprotective capacity accounts for succession and vertical distribution of marine phytoplankton species/groups. A model describing xanthophyll photoprotective activity in phytoplankton has been implemented in the European Regional Sea Ecosystem Model and applied at the station L4 in the Western English Channel. Primary producers were subdivided into three phytoplankton functional types defined in terms of their capacity to acclimate to different light-specific environments: low light (LL-type), high light (HL-type) and variable light (VL-type) adapted species. The LL-type is assumed to have low cellular level of xanthophyll-cycling pigments (PX) relative to the modelled photosynthetically active pigments (chlorophyll and fucoxanthin (FUCO) = PSP). The HL-type has high PX content relative to PSP while VL-type presents an intermediate PX to PSP ratio. Furthermore, the VL-type is capable of reversibly converting FUCO to PX and synthesizing new PX under high-light stress. In order to reproduce phytoplankton community succession with each of the three groups being dominant in different periods of the year, we had also to assume reduced grazing pressure on HL-adapted species. Model simulations realistically reproduce the observed seasonal patterns of pigments and nutrients highlighting the reasonability of the underpinning assumptions. Our model suggests that pigment-mediated photophysiology plays a primary role in determining the evolution of marine phytoplankton communities in the winter-spring period corresponding to the shoaling of the mixed layer and the increase of light intensity. Grazing selectivity however contributes to the phytoplankton community composition in summer.

Modelling the time-evolution of phytoplankton size spectra from satellite remote sensing

A dynamic size-structured model is developed for phytoplankton and nutrients in the oceanic mixed layer and applied to extract phytoplankton biomass at discrete size fractions from remotely sensed, ocean-colour data. General relationships between cell size and biophysical processes (such as sinking, grazing, and primary production) of phytoplankton were included in the model through a bottom–up approach. Time-dependent, mixed-layer depth was used as a forcing variable, and a sequential data-assimilation scheme was implemented to derive model trajectories. From a given time-series, the method produces estimates of size-structured biomass at every observation, so estimates seasonal succession of individual phytoplankton size, derived here from remote sensing for the first time. From these estimates, normalized phytoplankton biomass size spectra over a period of 9 years were calculated for one location in the North Atlantic. Further analysis demonstrated that strong relationships exist between the seasonal trends of the estimated size spectra and the mixed-layer depth, nutrient biomass, and total chlorophyll. The results contain useful information on the time-dependent biomass flux in the pelagic ecosystem.

Phytoplankton versus macrophyte contribution to primary production and biogeochemical cycles of a coastal mesotidal system. A modelling approach

This study presents an assessment of the contributions of various primary producers to the global annual production and N/P cycles of a coastal system, namely the Arcachon Bay, by means of a numerical model. This 3D model fully couples hydrodynamic with ecological processes and simulates nitrogen, silicon and phosphorus cycles as well as phytoplankton, macroalgae and seagrasses. Total annual production rates for the different components were calculated for different years (2005, 2007 and 2009) during a time period of drastic reduction in seagrass beds since 2005. The total demand of nitrogen and phosphorus was also calculated and discussed with regards to the riverine inputs. Moreover, this study presents the first estimation of particulate organic carbon export to the adjacent open ocean. The calculated annual net production for the Arcachon Bay (except microphytobenthos, not included in the model) ranges between 22,850 and 35,300 tons of carbon. The main producers are seagrasses in all the years considered with a contribution ranging from 56% to 81% of global production. According to our model, the -30% reduction in seagrass bed surface between 2005 and 2007, led to an approximate 55% reduction in seagrass production, while during the same period of time, macroalgae and phytoplankton enhanced their productions by about +83% and +46% respectively. Nonetheless, the phytoplankton production remains about eightfold higher than the macroalgae production. Our results also highlight the importance of remineralisation inside the Bay, since riverine inputs only fulfill at maximum 73% nitrogen and 13% phosphorus demands during the years 2005, 2007 and 2009. Calculated advection allowed a rough estimate of the organic matter export: about 10% of the total production in the bay was exported, originating mainly from the seagrass compartment, since most of the labile organic matter was remineralised inside the bay.

A modelling approach to the development of an active management strategy for the Queen Elizabeth II reservoir

This article outlines the outcome of work that set out to provide one of the specified integral contributions to the overarching objectives of the EU- sponsored LIFE98 project described in this volume. Among others, these included a requirement to marry automatic monitoring and dynamic modelling approaches in the interests of securing better management of water quality in lakes and reservoirs. The particular task given to us was to devise the elements of an active management strategy for the Queen Elizabeth II Reservoir. This is one of the larger reservoirs supplying the population of the London area: after purification and disinfection, its water goes directly to the distribution network and to the consumers. The quality of the water in the reservoir is of primary concern, for the greater is the content of biogenic materials, including phytoplankton, then the more prolonged is the purification and the more expensive is the treatment. Whatever good that phytoplankton may do by way of oxygenation and oxidative purification, it is eventually relegated to an impurity that has to be removed from the final product. Indeed, it has been estimated that the cost of removing algae and microorganisms from water represents about one quarter of its price at the tap. In chemically fertile waters, such as those typifying the resources of the Thames Valley, there is thus a powerful and ongoing incentive to be able to minimise plankton growth in storage reservoirs. Indeed, the Thames Water company and its predecessor undertakings, have a long and impressive history of confronting and quantifying the fundamentals of phytoplankton growth in their reservoirs and of developing strategies for operation and design to combat them. The work to be described here follows in this tradition. However, the use of the model PROTECH-D to investigate present phytoplankton growth patterns in the Queen Elizabeth II Reservoir questioned the interpretation of some of the recent observations. On the other hand, it has reinforced the theories underpinning the original design of this and those Thames-Valley storage reservoirs constructed subsequently. The authors recount these experiences as an example of how simulation models can hone the theoretical base and its application to the practical problems of supplying water of good quality at economic cost, before the engineering is initiated.