Light-limited growth of two strains of the marine diatom Phaeodactylum tricornutum Bohlin: Chemical composition, carbon partitioning and the diel periodicity of physiological processes

Terry, Kenneth L.; Hirata, Janice; Laws, Edward A.

doi:10.1016/0022-0981(83)90054-0

Cited by 90 publications

(50 citation statements)

References 45 publications

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“…Elemental composition in growth limited by P (N t :P t = 75), N (N t :P t = 15) and light (N t :P t = 6). Data from Terry et al (1983Terry et al ( , 1985 Thus, it appears that the model was able to reproduce the observations from the different experiments with varying success. However, discrepancies between model and data are not greater than discrepancies among the experiments.…”

Section: Resultsmentioning

confidence: 99%

Chain model of phytoplankton P, N and light colimitation

Pahlow¹,

Oschlies²

2009

Mar. Ecol. Prog. Ser.

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Section: Resultsmentioning

confidence: 99%

Chain model of phytoplankton P, N and light colimitation

Pahlow¹,

Oschlies²

2009

Mar. Ecol. Prog. Ser.

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“…So far, there is a limited number of experimental studies whose data were used to calibrate these acclimation models (Laws and Bannister, 1980;Terry et al, 1983Terry et al, , 1985Healey, 1985;Flynn et al, 1994;Anning et al, 2000). Model calibrations were usually done by tuning parameter values so that model solutions provide a qualitative good fit to the laboratory data.…”

Section: Constraining Simulations Of Algal Growth With Laboratory Andmentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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“…Of particular interest for this discussion is the low ratio of standard error to mean which indicates little interspecific variation in the light requirements for growth within the diatoms. Where observations of the p-I curve are available for more than 1 species or clone under the same experimental conditions (Chan 1978, Nelson et al 1978, Terry et al 1983) plots of relative growth rate (p/pm) against PFD show only minor differences. Although occasionally of poor resolution, these observations indicate that most species of diatoms can be characterized by the same value of I, independent of cell size.…”

Section: Light Curve Of Growthmentioning

confidence: 99%

Size dependence of growth and photosynthesis in diatoms: a synthesis

Geider¹,

Piatt²,

Raven³

1986

Mar. Ecol. Prog. Ser.

190

130

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The dependence of growth and photosythesis on cell size in diatoms is evaluated in terms of an energy-balance model of microalgal physiology. Based on a review of available observations it appears that cell size can account for much of the interspecific variability in maximum growth rate ( p, , , ) and in optical absorption cross-section (ach,). Both CL, and a,,, decrease with increases in cell size. Cell size does not appear to influence the chlorophyll a:carbon ratio, the quantum efficiency of photosynthesis nor the photon flux density (PFD) at which growth rate is llght-saturated. Available observations do not allow an evaluation of the size dependence of maintenance metabolic rates or the PFD at which light compensation of growth occurs. This analysis confirms the competitive advantage of small cell size of microalgae under nutrient-sufficient conditions at both light-saturating and light-limiting growth rates to the extent that small-celled diatoms have enhanced catalytic efficiencies of growth and light absorption.

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Light-limited growth of two strains of the marine diatom Phaeodactylum tricornutum Bohlin: Chemical composition, carbon partitioning and the diel periodicity of physiological processes

Cited by 90 publications

References 45 publications

Chain model of phytoplankton P, N and light colimitation

Chain model of phytoplankton P, N and light colimitation

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Size dependence of growth and photosynthesis in diatoms: a synthesis

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