Key drivers of seasonal plankton dynamics in cyclonic and anticyclonic eddies off East Australia

© 2016 Laiolo, McInnes, Matear and Doblin. Mesoscale eddies in the south west Pacific region are prominent ocean features that represent distinctive environments for phytoplankton. Here, we examine the seasonal plankton dynamics associated with averaged cyclonic and anticyclonic eddies (CE and ACE, respectively) off eastern Australia. We do this through building seasonal climatologies of mixed layer depth (MLD) and surface chlorophyll-a for both CE and ACE by combining remotely sensed sea surface height (TOPEX/Poseidon, Envisat, Jason-1, and OSTM/Jason-2), remotely sensed ocean color (GlobColour) and in situ profiles of temperature, salinity and pressure from Argo floats. Using the CE and ACE seasonal climatologies, we assimilate the surface chlorophyll-a data into both a single (WOMBAT), and multi-phytoplankton class (EMS) biogeochemical model to investigate the level of complexity required to simulate the phytoplankton chlorophyll-a. For the two eddy types, the data assimilation showed both biogeochemical models only needed one set of parameters to represent phytoplankton but needed different parameters for zooplankton. To assess the simulated phytoplankton behavior we compared EMS model simulations with a ship-based experiment that involved incubating a winter phytoplankton community sampled from below the mixed layer under ambient and two higher light intensities with and without nutrient enrichment. By the end of the 5-day field experiment, large diatom abundance was four times greater in all treatments compared to the initial community, with a corresponding decline in pico-cyanobacteria. The experimental results were consistent with the simulated behavior in CE and ACE, where the seasonal deepening of the mixed layer during winter produced a rapid increase in large phytoplankton. Our model simulations suggest that CE off East Australia are not only characterized by a higher chlorophyll-a concentration compared to ACE, but also by a higher concentration of large phytoplankton (i.e., diatoms) due to the shallower CE mixed layer. The model simulations also suggest the zooplankton community is different in the two eddy types and this behavior needs further investigation

Using satellite ocean colour to explore phytoplankton dynamics and size in East Australian waters

University of Technology Sydney. Faculty of Science.The eastern Australian ocean region is strongly influenced by the East Australian Current (EAC). Waters in this region are generally oligotrophic; despite this, nutrient enrichment and phytoplankton blooms occur as a response to physical events such as the seasonal deepening of the mixed layer or the formation of cyclonic eddies. In this PhD project, biogeochemical and optical modelling, ocean color data assimilation, in situ measurements and ship-board experiments were used to investigate phytoplankton dynamics and size structure in offshore eastern Australian waters, information that is necessary to improve estimates of future ocean primary productivity. First, the seasonal phytoplankton dynamics in averaged cyclonic and anticyclonic eddies (CE and ACE, respectively) off eastern Australia were explored through a single and a multi-phytoplankton class biogeochemical model. Seasonal climatologies of surface chlorophyll-a concentration (Chl-a) and mixed layer depth for both CE and ACE were obtained by combining remotely sensed sea surface height, remotely sensed ocean color and in situ profiles from Argo floats. Simulated phytoplankton responses to changes in nutrients and light were compared with a ship-based experiment. The experimental results were consistent with the model result, where the seasonal deepening of the mixed layer during winter produced a rapid increase in large phytoplankton. Although the Chl-a concentration in CE was larger than ACE, the primary production estimates obtained through the assimilation of the ocean colour product within different types of eddies were similar, showing an inconsistency with previously published studies that suggest CE are significantly more productive. To explore the properties and relationship of the satellite ocean colour product and in situ observations, theoretical experiments were performed through a coupled biogeochemical-optical model. Specifically, an optical model was used to calculate the inherent optical properties (IOPs) of seawater from size dependent multi-phytoplankton biogeochemical model simulations and convert them into remote-sensing reflectance (R). Then, R was used to produce a satellite-like estimate of the simulated surface Chl-a concentration through the OC3M algorithm. The information content of simulated in situ and simulated remotely-sensed data sources was investigated through theoretical experiments that suggested the OC3M algorithm underestimates the simulated Chl-a concentration because of the weak relationship between large-sized phytoplankton and R. Finally, this concept was tested with real data collected on a voyage in 2016, to investigate the relationship between the in situ sampled phytoplankton size structure and the corresponding satellite Chl-a product. Ocean colour match-up points confirmed the underestimation of in situ Chl-a concentrations when phytoplankton larger than 10 μm dominated the photosynthetic community. Furthermore, optical model simulations suggested that large phytoplankton cells cause a decrease in both the absorption and backscattering signals, which in turn affect the R and cause the underestimation of Chl-a by the satellite Chl-a product. To understand impacts of contemporary ocean change on regional primary productivity, we rely on biogeochemical models to scale up sparse in situ observations. Although ocean colour provides information at high spatial and temporal resolution, this information has limited accuracy. Results presented in this thesis show that a simultaneous assimilation of in situ and satellite remote sensing can provide additional information about the phytoplankton size structure, crucial data to progress our understanding of processes influencing regional primary productivity and elemental cycling. Therefore, parameter optimization through a combination of the information provided by two distinct observation platforms (in situ and satellite remote sensing) will lead to the development of next-generation biogeochemical models

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

Phytoplankton dynamics in the Gulf of Aqaba (Eilat, Red Sea): A simulation study of mariculture effects

none8siThe northern Gulf of Aqaba is an oligotrophic water body hosting valuable coral reefs. In the Gulf, phytoplankton dynamics are driven by an annual cycle of stratification and mixing. Superimposed on that fairly regular pattern was the establishment of a shallow-water fish-farm initiative that increased gradually until its activity was terminated in June 2008. Nutrient, water temperature, irradiation, phytoplankton data gathered in the area during the years 2007-2009, covering the peak of the fish-farm activity and its cessation, were analyzed by means of statistical analyses and ecological models of phytoplankton dynamics. Two datasets, one from an open water station and one next to the fish farms, were used. Results show that nutrient concentrations and, consequently, phytoplankton abundance and seasonal succession were radically altered by the pollution originating from the fish-farm in the sampling station closer to it, and also that the fish-farm might even have influenced the open water station.openLaiolo, L.; Barausse, A.; Dubinsky, Z.; Palmeri, L.; Goffredo, S.; Kamenir, Y.; Al Najjar, T.; Iluz, D.Laiolo, L.; Barausse, A.; Dubinsky, Z.; Palmeri, L.; Goffredo, S.; Kamenir, Y.; Al Najjar, T.; Iluz, D

Live cell analysis at sea reveals divergent thermal performance between photosynthetic ocean microbial eukaryote populations

Experimentation at sea provides insight into which traits of ocean microbes are linked to performance in situ. Here we show distinct patterns in thermal tolerance of microbial phototrophs from adjacent water masses sampled in the south-west Pacific Ocean, determined using a fluorescent marker for reactive oxygen species (ROS). ROS content of pico-eukaryotes was assessed after 1, 5 and 25 h of incubation along a temperature gradient (15.6–32.1 °C). Pico-eukaryotes from the East Australian Current (EAC) had relatively constant ROS and showed greatest mortality after 25 h at 7 °C below ambient, whereas those from the Tasman Sea had elevated ROS in both warm and cool temperature extremes and greatest mortality at temperatures 6–10 °C above ambient, interpreted as the outcome of thermal stress. Tracking of water masses within an oceanographic circulation model showed populations had distinct thermal histories, with EAC pico-eukaryotes experiencing higher average temperatures for at least 1 week prior to sampling. While acclimatization and community assembly could both influence biological responses, this study clearly demonstrates that phenotypic divergence occurs along planktonic drift trajectories

CSIRO Environmental Modelling Suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0)

Since the mid-1990s, Australia's Commonwealth Science Industry and Research Organisation (CSIRO) has been developing a biogeochemical (BGC) model for coupling with a hydrodynamic and sediment model for application in estuaries, coastal waters and shelf seas. The suite of coupled models is referred to as the CSIRO Environmental Modelling Suite (EMS) and has been applied at tens of locations around the Australian continent. At a mature point in the BGC model's development, this paper presents a full mathematical description, as well as links to the freely available code and user guide. The mathematical description is structured into processes so that the details of new parameterisations can be easily identified, along with their derivation. In EMS, the underwater light field is simulated by a spectrally resolved optical model that calculates vertical light attenuation from the scattering and absorption of 20+ optically active constituents. The BGC model itself cycles carbon, nitrogen, phosphorous and oxygen through multiple phytoplankton, zooplankton, detritus and dissolved organic and inorganic forms in multiple water column and sediment layers. The water column is dynamically coupled to the sediment to resolve deposition, resuspension and benthic-pelagic biogeochemical fluxes. With a focus on shallow waters, the model also includes detailed representations of benthic plants such as seagrass, macroalgae and coral polyps. A second focus has been on, where possible, the use of geometric derivations of physical limits to constrain ecological rates. This geometric approach generally requires population-based rates to be derived from initially considering the size and shape of individuals. For example, zooplankton grazing considers encounter rates of one predator on a prey field based on summing relative motion of the predator with the prey individuals and the search area; chlorophyll synthesis includes a geometrically derived self-shading term; and the bottom coverage of benthic plants is calculated from their biomass using an exponential form derived from geometric arguments. This geometric approach has led to a more algebraically complicated set of equations when compared to empirical biogeochemical model formulations based on populations. But while being algebraically complicated, the model has fewer unconstrained parameters and is therefore simpler to move between applications than it would otherwise be. The version of EMS described here is implemented in the eReefs project that delivers a near-real-time coupled hydrodynamic, sediment and biogeochemical simulation of the Great Barrier Reef, northeast Australia, and its formulation provides an example of the application of geometric reasoning in the formulation of aquatic ecological processes. </p

CSIRO Environmental Modelling Suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0)

Since the mid 1990s, Australia's Commonwealth Science Industry and Research Organisation (CSIRO) has developed a biogeochemical (BGC) model for coupling with a hydrodynamic and sediment model for application in estuaries, coastal waters and shelf seas. The suite of coupled models is referred to as the CSIRO Environmental Modelling Suite (EMS) and has been applied at tens of locations around the Australian continent. At a mature point in the BGC model's development, this paper presents a full mathematical description, as well as links to the freely available code and User Guide. The mathematical description is structured into processes so that the details of new parameterisations can be easily identified, along with their derivation. The EMS BGC model cycles carbon, nitrogen, phosphorous and oxygen through multiple phytoplankton, zooplankton, detritus and dissolved organic and inorganic forms in multiple water column and sediment layers. The underwater light field is simulated by a spectrally-resolved optical model that includes the calculation of water-leaving reflectance for validation with remote sensing. The water column is dynamically coupled to the sediment to resolve deposition, resuspension and benthic-pelagic biogeochemical fluxes. With a focus on shallow waters, the model also includes particularly-detailed representations of benthic plants such as seagrass, macroalgae and coral polyps. A second focus has been on, where possible, the use of geometric derivations of physical limits to constrain ecological rates, which generally requires population-based rates to be derived from initially considering the size and shape of individuals. For example, zooplankton grazing considers encounter rates of one predator on a prey field based on summing relative motion of the predator with the prey individuals and the search area, chlorophyll synthesis includes a geometrically-derived self-shading term, and the bottom coverage of benthic plants is generically-related to their biomass using an exponential form derived from geometric arguments. This geometric approach has led to a more algebraically-complicated set of equations when compared to more empirical biogeochemical model formulations. But while being algebraically-complicated, the model has fewer unconstrained parameters and is therefore simpler to move between applications than it would otherwise be. The version of the biogeochemistry described here is implemented in the eReefs project that is delivering a near real time coupled hydrodynamic, sediment and biogeochemical simulation of the Great Barrier Reef, northeast Australia, and its formulation provides an example of the application of geometric reasoning in the formulation of aquatic ecological processes