Dataset on the absorption characteristics of extracted phytoplankton pigments

Clementson, Lesley; Wojtasiewicz, Bożena

doi:10.1016/j.dib.2019.103875

Cited by 50 publications

(49 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…a(λ) is the concentration specific absorption coefficient in m 2 g −1 , its values were obtained from the literature (Clementson and Wojtasiewicz, 2019). C is the pigment specific average concentration within all contact cores in mg.g −1 .…”

Section: Absorption Spectramentioning

confidence: 99%

Effect of Light Intensity and Light Quality on Diatom Behavioral and Physiological Photoprotection

et al. 2020

View full text Add to dashboard Cite

In this study, we investigated the different photoregulation responses of diatom dominated natural biofilms to different light intensities and wavelengths, over a tidal cycle in the laboratory. We compared the overall effect of light spectral quality from its light absorption (Qphar) dependent effect. Two different conditions were compared to study photoprotective strategies: sediment (migrational) and without sediment (non-migrational). Three different colors (blue, green, and red) and two light intensities (low light, LL at 210 µmol.photons.m −2 .s −1 and high light, HL at 800 µmol.photons.m −2 .s −1) showed strong interactions in inducing behavioral and physiological photoprotection. Non-migrational biofilm non-photochemical quenching (NPQ) was much more reactive to blue HL than red HL while it did not differ in LL. We observed a biphasic NPQ response with a light threshold between 200 and 250 µmol.photons.m −2 .s −1 of Qphar that elicited the onset of physiological photoprotection. Similar HL differences were not observed in migrational biofilms due to active vertical migration movements that compensated light saturating effects. Our results showed that within migrational biofilms there was an interaction between light quality and light intensity on cell accumulation pattern at the sediment surface. This interaction led to inverse diatom accumulation patterns between blue and red light at the same intensity: LL (blue + 200.67%, red + 123.96%), HL (blue + 109.15%, red + 150.34%). These differences were largely related to the differential amount of light absorbed at different wavelengths and highlighted the importance of using wavelength standardized intensities. Different vertical migration patterns significantly affected the total pigment content measured at the surface, suggesting that cell could migrate downward more than 2 mm as a photoregulatory response. Colloidal carbohydrates patterns paralleled the vertical migration movements, highlighting their possible role in diatom motility. Our data strongly suggests a wavelength and Qphar dependent light stress threshold that triggers upward and downward movements to position microphytobenthic diatoms at their optimal depth.

show abstract

Section: Absorption Spectramentioning

confidence: 99%

Effect of Light Intensity and Light Quality on Diatom Behavioral and Physiological Photoprotection

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The model contains 4 phytoplankton types (small and large phytoplankton, benthic mircoalgae and Trichodesmium), each with a unique ratio of internal concentration of accessory photosynthetic pigments to chlorophyll-a. To calculate the absorption due to each pigment, we use a database of spectrally-resolved mass-specific absorption coefficients (Clementson and Wojtasiewicz, 2019). As it can be assumed that accessory pigments stay in a constant ratio to chlorophyll-a, the model needs only a state variable for chlorophyll-a for each phytoplankton type.…”

Section: Inherent Optical Properties (Iops)mentioning

confidence: 99%

“…3. Mass-specific absorption coefficients of photosynthetic pigments have been better utilised to determine phytoplankton absorption cross-sections (Duysens, 1956;Kirk, 1975;Morel and Bricaud, 1981) through the availability of a library of mass-specific absorption coefficients (Clementson and Wojtasiewicz, 2019), and their wavelength correction using the refractive index of the solvent used in the laboratory determinations ( Fig. 6).…”

Section: Introductionmentioning

confidence: 99%

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

Baird¹,

Wild-Allen²,

Parslow³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. 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.

show abstract

“…The model contains four phytoplankton types (small and large phytoplankton, benthic microalgae and Trichodesmium), each with a unique ratio of internal concentration of accessory photosynthetic pigments to chlorophyll a. To calculate the absorption due to each pigment, we use a database of spectrally resolved mass-specific absorption coefficients (Clementson and Wojtasiewicz, 2019). As it can be assumed that accessory pigments stay in a constant ratio to chlorophyll a, the model needs only a state variable for chlorophyll a for each phytoplankton type.…”

Section: Inherent Optical Properties (Iops)mentioning

confidence: 99%

“…3. mass-specific absorption coefficients of photosynthetic pigments better utilised to determine phytoplankton absorption cross-sections (Duysens, 1956;Kirk, 1975;Morel and Bricaud, 1981) through the availability of a library of mass-specific absorption coefficients (Clementson and Wojtasiewicz, 2019) and their wavelength correction using the refractive index of the solvent used in the laboratory determinations (Fig. 5);…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Abstract. 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.

show abstract

Dataset on the absorption characteristics of extracted phytoplankton pigments

Cited by 50 publications

References 3 publications

Effect of Light Intensity and Light Quality on Diatom Behavioral and Physiological Photoprotection

Effect of Light Intensity and Light Quality on Diatom Behavioral and Physiological Photoprotection

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

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

Contact Info

Product

Resources

About