Sensitivity of the Indian Ocean circulation to phytoplankton forcing using an ocean model

Most ocean general circulation models (OGCMs) do not take into account the effect of space- and time-varying phytoplankton on solar radiation penetration, or do it in a simplistic way using a constant attenuation depth, even though one-dimensional experiments have shown potential significant effect of phytoplankton on mixed-layer dynamics. Since some ocean basins are biologically active, it is necessary for an OGCM to take water turbidity into account, even if it is not coupled with a biological model. Sensitivity experiments carried out with the Massachusetts Institute of Technology (MIT) OGCM with spatially and temporally-varying pigment concentration from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data during 1998-2003 reveal the effect of ocean turbidity on tropical Indian Ocean circulation. Variations of light-absorbing phytoplankton pigments change the vertical distribution of solar heating in the mixed layer, thereby affecting upper-ocean circulation. A simulation was performed from 1948 to 2003 with a constant minimum pigment concentration of 0.02 mg m^− 3 while another simulation was performed from September 1997 to December 2003 with variable pigment concentration, and the differences between these two simulations allow us to quantify the effects of phytoplankton on solar radiation penetration in the ocean model. Model results from a period of 6 years (1998-2003) show large seasonal variability in the strength of the meridional overturning circulation (MOC), meridional heat transports (MHT), and equatorial under current (EUC). The MOC mass transport changes by 2 to 5 Sv (1 Sv = 10⁶ m³ s^− 1) between boreal winter (January) and boreal summer (July), with a corresponding change in the MHT of ∼ 0.05 PW (1 PW = 10¹⁵ W) in boreal winter, which is close to the expected change associated with a significant climate change [Shell, K., Frouin, R., Nakamoto, S., & Somerville, R.C.J. (2003): Atmospheric response to solar radiation absorbed by phytoplankton. Journal of Geophysical Research, 108(D15), 4445. doi:10.1029/2003JD003440.]. In addition, changes in phytoplankton pigments concentration are associated with a reduction in the EUC by ∼ 3 cm s^− 1. We discuss the possible physical mechanisms behind this variability, and the necessity of including phytoplankton forcing in the OGCM.     

Causes of phytoplankton changes in Saginaw Bay,Lake Huron,during the zebra mussel invasion

Colonization of the Laurentian Great Lakes by the invasive mussel Dreissena polymorpha was a significant ecological disturbance. The invasion reached Saginaw Bay, Lake Huron, in 1991 and initially cleared the waters and lowered algal biomass. However, an unexpected result occurred 3 years after the initial invasion with the return of nuisance summer blooms of cyanobacteria, a problem that had been successfully addressed with the implementation of phosphorus controls in the late 1970s. A multi-class phytoplankton model was developed and tested against field observations and then used to explore the causes of these temporal changes. Model scenarios suggest that changes in the phytoplankton community can be linked to three zebra mussel-mediated effects: (1) removal of particles resulting in clearer water, (2) increased recycle of available phosphorus throughout the summer, and (3) selective rejection of certain Microcystis strains. Light inhibition of certain phytoplankton assemblages and the subsequent alteration of competitive dynamics is a novel result of this model. These results enhance our understanding of the significant role of zebra mussels in altering lower trophic level dynamics of Saginaw Bay and suggest that their physical re-engineering of the aquatic environment was the major force driving changes in the phytoplankton community composition.     

The contribution of phytoplankton degradation to chromophoric dissolved organic matter (CDOM) in eutrophic shallow lakes: Field and experimental evidence

Eight field campaigns in the eutrophic, shallow, Lake Taihu in the summers from 2005 to 2007, and a phytoplankton degradation experiment of 33 days, were carried out to determine the contribution of phytoplankton degradation to CDOM. Significant and positive correlations were found between the CDOM absorption coefficient at 355 nm [a_CDOM(355)], normalized fluorescence emission (QSU) at 450 nm from excitation at 355 nm [F_n(355)], and the chlorophyll a (Chla) concentration for all eight field campaigns, which indicates that the decomposition and degradation of phytoplankton is an important source of CDOM. In the degradation experiment, the CDOM absorption coefficient increased as phytoplankton broke down during the first 12 days, showing the production of CDOM from phytoplankton. After 12 days, a_CDOM(355) had increased from the initial value 0.41 ± 0.03 m⁻¹ to 1.37 ± 0.03 m⁻¹ (a 234% increase), and the Chla concentration decreased from the initial value of 349.1 ± 11.2 μg/L to 30.4 ± 13.2 μg/L (a 91.3% decrease). The mean daily production rate of CDOM from phytoplankton was 0.08 m⁻¹ for a_CDOM(355). Parallel Factor Analysis (PARAFAC) was used to assess CDOM composition from EEM spectra, and four components were identified: a terrestrial-like humic component, two marine-like humic components, and a protein-like component. The rapid increase in marine-like humic fluorophores (C3 and C4) during the degradation experiment suggests that in situ production of CDOM plays an important role in the dynamics of CDOM. The field campaigns and experimental data in the present study show that phytoplankton can be one of the important CDOM producers in eutrophic shallow lakes.     

Patterns and mechanisms of phytoplankton variability in Lake Washington (USA)

Temporal variability in lake phytoplankton is controlled largely by a complex interplay between hydrodynamic and chemical factors, and food web interactions. We explored mechanisms underlying phytoplankton interannual variability in Lake Washington (USA), using a 25-yr time series of water quality data (1975-1999). Time-series analysis and PCA were used to decompose chlorophyll data into modes of variability. We found that phytoplankton dynamics in Lake Washington were characterized by four seasonal modes, each of which was associated with different ecological processes. The first mode coincided with the period when the system was light limited (January-March) and phytoplankton patterns were driven by the amount of available solar radiation. The second mode (April-June) coincided with the peak of the spring bloom and the subsequent decline of phytoplankton biomass, and was largely controlled by total phosphorus levels and grazing pressure from cladoceran zooplankton. Evidence of co-dependence and tight relationship between phytoplankton and cladoceran dynamics were also found from July to October when a large portion of the phosphorus supply in the mixed layer was provided by zooplankton excretion. The fourth mode (November-December) was associated with the transition to thermal and chemical homogeneity and the winter phytoplankton minima (2-2.5 microg/l). Finally, we examined the effects of meteorological forcing and large-scale oceanic climate fluctuations (ENSO and PDO) on phytoplankton dynamics and assessed the significance of their role on the interannual variability in the lake.     

Temporal and spatial variability of phytoplankton in Lake Poyang: The largest freshwater lake in China

The composition and both the temporal and spatial distribution of phytoplankton were studied in Lake Poyang; samples were collected every 3 months from January 2009 to October 2011 at 15 sites. The phytoplankton community was found to belong to seven groups, with Bacillariophyta dominating. No significant difference was observed in the phytoplankton community structure at any of the sites (p = 0.2371), except one site; however, the structure was significantly different with regard to annual and seasonal trends (p = 0.0001 and p < 0.0001, respectively). Aulacoseira granulata, Synedra acus, Fragilaria virescens, and Cryptomonas erosa were the main contributors to the dissimilarity in temporal distribution. Although the nutrient concentrations for 3 years combined were relatively high (mean total nitrogen was 1.719 mg L^− 1 and mean total phosphorus was 0.090 mg L^− 1), phytoplankton biomass was low (mean total biomass of 0.203 mg L^− 1). The underwater light condition, as indicated by the Secchi depth, was shown to be the principal limiting factor in regulating the growth of phytoplankton, and the transparency coincided with biomass variation on a seasonal level. The effect of nutrients on phytoplankton may be concealed by the water level, which varied over a wide range among different seasons. However, the annual trend for the biomass was associated with the nutrient concentration, which increased yearly and initiated the development of phytoplankton. The biomass is high in the south and low in the north, which may be the result of greater underwater light climate and high nutrient concentrations in the southern area.     

Influence of phytoplankton pigment composition on remote sensing of cyanobacterial biomass

An extensive field campaign was carried out for the validation of a previously published reflectance ratio-based algorithm for quantification of the cyanobacterial pigment phycocyanin (PC). The algorithm uses band settings of the Medium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, and should accurately retrieve the PC concentration in turbid, cyanobacteria-dominated waters. As algae and cyanobacteria often co-occur, the algorithm response to varying phytoplankton composition was explored. Remote sensing reflectance and reference pigment measurements were obtained in the period 2001-2005 in Spain and the Netherlands using field spectroradiometry and various pigment extraction methods. Additional field data was collected in Spain in May 2005 to allow intercalibration of spectroradiometry and pigment assessment methods. Two methods for extraction of PC from concentrated water samples, and in situ measured PC fluorescence, compared well. Reflectance measurements with different field spectroradiometers used in Spain and the Netherlands also gave similar results. Residual analysis of PC predicted by the algorithm showed that overestimation of PC mainly occurred in the presence of chlorophylls b and c, and phaeophytin. The errors were strongest at low PC relative to Chl a concentrations. A correction applied for absorption by Chl b markedly improved the prediction. Without such a correction, the quality of the PC prediction still increased markedly with estimates > 50 mg PC m^− 3, allowing monitoring of the cyanobacterial status of eutrophic waters. The threshold concentration may be lowered when a high intracellular PC:Chl a ratio or cyanobacterial dominance is expected. Below the limit, predicted PC concentrations should be considered as the highest estimate. We evaluated that remote sensing of both PC and Chl a would allow assessment of cyanobacterial risk to water quality and public health in over 70% of our cases.     

Remote sensing of phytoplankton pigment distribution in the United States northeast coast

Phytoplankton pigments constitute many more compounds than chlorophyll a that can be applied to study phytoplankton diversity, populations, and primary production. In this study, field measurements were applied to develop ocean color satellite algorithms of phytoplankton pigments from in-water radiometry measurements. The match-up comparisons showed that the satellite-derived pigments from our algorithms agree reasonably well (e.g. 30-55% of uncertainty for SeaWiFS and 37-50% for MODIS-Aqua) to field data, with better agreement (e.g. 30-38% of uncertainty for SeaWiFS and 39-44% for MODIS-Aqua) for pigments abundant in diatoms. The seasonal and spatial variations of satellite-derived phytoplankton biomarker pigments, such as fucoxanthin, which is abundant in diatoms, peridinin, which is found only in peridinin-containing dinoflagellates, and zeaxanthin, which is primarily from cyanobacteria in coastal waters, revealed that higher densities of diatoms are more likely to occur on the inner shelf and during winter-spring and obscure other abundant phytoplankton groups. However, relatively higher densities of other phytoplankton, such as dinoflagellates and cyanobacteria, are likely to occur on the mid- to outer-continental shelf and during summer. Seasonal variation of riverine discharge may play an important role in stimulating algal blooms, in particular diatoms, while higher abundances of cyanobacteria coincide with warmer water temperatures and lower nutrient concentrations.     

Assessing phytoplankton structure and spatio-temporal dynamics in a freshwater ecosystem using a powerful multiway statistical analysis

Phytoplankton dynamics and diversity are particularly difficult to analyze, especially when (i) the scale of the analysis is situated at the species level, (ii) such a diversity is high, (iii) the study covers several seasons, and (iv) sampling has been performed at many stations of the ecosystem. Fortunately, some powerful statistical methods have been developed with which each species identified can be considered in detailed spatio-temporal analyses. The Partial Triadic Analysis, a method issued from the STATIS family, was applied on a dataset corresponding to 6 stations of the largest French reservoir (Reservoir Marne) sampled 22 times over two years (2006-2007) between March and September. Three key sampling periods that were consistent with those exhibited with the Plankton Ecology Group model (i.e. early spring, late spring-early summer, late summer-early autumn) were unambiguously recognized, with some specific species associated with each of them. Furthermore, a potential reference sampling station was identified among all stations investigated, an information very relevant to both scientists and water managers. It remains that 3 other stations could also be monitored, regularly or from time to time, because of specific phytoplankton characteristics.     

A sustainable, carbon neutral methane oxidation by a partnership of methane oxidizing communities and microalgae

Effluents of anaerobic wastewater treatment plants are saturated with methane, an effective greenhouse gas. We propose a novel approach to treat such effluents using a coculture of methane oxidizing communities and microalgae, further indicated as methalgae, which would allow microbial methane oxidation with minimal CO₂ emissions. Coculturing a methane oxidizing community with microalgae in sequence batch reactors under continuous lightning yielded a factor of about 1.6 more biomass relative to the control without microalgae. Moreover, 55% less external oxygen supply was needed to maintain the methane oxidation, as oxygen was produced in situ by the microalgae. An overall methane oxidation rate of 171 ± 27 mg CH₄ L⁻¹ liquid phase d⁻¹ was accomplished in a semi-batch setup, while the excess CO₂ production was lower than 1 mg CO₂ L⁻¹ d⁻¹. Both nitrate and ammonium were feasible nitrogen sources for the methalgae. These results show that a coculture of microalgae and methane oxidizing communities can be used to oxidize dissolved methane under O₂-limiting conditions, which could lead to a novel treatment for dissolved methane in anaerobic effluents.     

Determination of biogeochemical parameters in eutrophication models with simultaneous dynamic optimization approaches

This work addresses a parameter estimation problem in an ecological water quality model through a simultaneous dynamic optimization approach. The model is based on first principles and has a large number of parameters, which must be estimated based on data collected in the water body under study. Gradients of state variables are considered along the water column, rendering a partial differential equation problem, which is transformed into a differential algebraic (DAE) one by spatial discretization in several water layers. Within a simultaneous approach, the DAE constrained optimization problem is transformed into a large-scale nonlinear programming problem, with a weighted least squares objective function. Main biogeochemical parameters have been obtained, which allow a close representation of the lake dynamics, as it is shown in the numerical results.