A theoretical and experimental examination of the predictions of two recent models of phytoplankton growth

Redalje, Donald G.; Karl, David M.; Chalup, Michael S.

doi:10.1016/0022-5193(83)90188-1

Cited by 26 publications

(13 citation statements)

References 18 publications

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“…These ratios vary with growth conditions including nutrient supply (Laws et al 1983), temperature (Berges et al 2002), and light (Finkel et al 2006), and the present results add to a growing body of evidence that major element ratios in phytoplankton also vary with CO 2 . In marine diatoms, present and prior results indicate that C:N ratios increase as pCO 2 increases from 150 to 380 ppm, but that these ratios are relatively constant at higher pCO 2 .…”

Section: Co 2 Regulation Of Element Ratios In Marine Phytoplanktonsupporting

confidence: 76%

“…Nitrogen in phytoplankton is primarily associated with protein and amino acids, although nucleic acids could account for up to 18% (Lourenço et al 1998). Phosphorus is used in assembly (ribosomal RNA), structural support (phospholipid), and storage (polyphosphate) components, but the phosphorus contents of marine phytoplankton are not well constrained by biochemical composition (Laws et al 1983, Geider & La Roche 2002. The additional nitrogen needed to support inorganic carbon acquisition in phytoplankton cells with CCMs through the production of membrane-bound inorganic carbon transporters and carbonic anhydrase was estimated to account for less than 1% of cellular nitrogen (Raven 1991, Raven & Johnston 1991.…”

Section: Discussionmentioning

confidence: 99%

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Carbon dioxide regulation of nitrogen and phosphorus in four species of marine phytoplankton

Reinfelder¹

2012

Mar. Ecol. Prog. Ser.

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The concentration of carbon dioxide in seawater may affect phytoplankton physiology and ecology and their role in marine biogeochemical cycles. In order to assess the effects of CO 2 on the elemental composition of marine phytoplankon, carbon, nitrogen, and phosphorus quotas were measured in 4 species of marine phytoplankton acclimated to 150 to 1500 ppm CO 2 (5 to 50 µM) in semi-continuous cultures. Nitrogen quotas declined steeply with increasing CO 2 in the centric diatoms Thalassiosira pseudonana and T. weissflogii acclimated to 150 to 380 ppm (5 to 13 µM), but more slowly as the CO 2 increased from 380 to 1500 ppm (13 to 50 µM). Nitrogen demand varied little with CO 2 in the pennate diatom Phaeodactylum tricornutum, but was positively correlated with CO 2 over the range of 150 to 770 ppm in the prymnesiophyte Isochrysis galbana. Based on the nitrogen−CO 2 trends in centric diatoms, relief from carbon−nitrogen colimitation could lead to 2-fold larger cells as CO 2 increases from 150 to 380 ppm, but only 15% larger cells from 380 to 770 ppm CO 2 . Phosphorus quotas in the 3 diatoms decreased as CO 2 increased from 150 to 380 ppm. As previously observed in these and other species, C:N, C:P, and N:P ratios increased with increasing CO 2 , but the present results show that much of this variation was due to differences in nitrogen and phosphorus rather than carbon quotas. Marine phytoplankton could provide a negative feedback against increasing CO 2 over the pCO 2 range of 150 to 380 ppm by supporting larger cells or higher biomass, but would support a smaller carbon sink as atmospheric CO 2 rises above 380 ppm. KEY WORDS: Nitrogen · Phosphorus · Carbon dioxide · PhytoplanktonResale or republication not permitted without written consent of the publisher

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Section: Co 2 Regulation Of Element Ratios In Marine Phytoplanktonsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Carbon dioxide regulation of nitrogen and phosphorus in four species of marine phytoplankton

Reinfelder¹

2012

Mar. Ecol. Prog. Ser.

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“…In all cases, both sets of incubations were carried out at a temperature of 20 • C. The light incubations were carried out at an irradiance of 400 µmol photons m −2 s −1 of 400-700 nm radiation provided by a bank of fluorescent lamps. We assumed this irradiance to be sufficient to saturate photosynthetic rates [17,18]. Oxygen concentrations were measured during the first eight months by Winkler titration [16] and during the final four months with a YSI model 5905 oxygen meter.…”

Section: Methodsmentioning

confidence: 99%

Nutrients and Phytoplankton in a Shallow, Hypereutrophic Urban Lake: Prospects for Restoration

Norris

2017

Water

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University Lake, a shallow, artificial, urban lake adjacent to the campus of Louisiana State University, has a long history of water quality problems, including algal blooms, fish kills, and high concentrations of fecal indicator bacteria. Periodic dredging of the lake is necessary to prevent its return to swampland. This study was undertaken to elucidate the roles of allochthonous versus autochthonous nutrients as causes of water quality problems in the lake, with the expectation that this information would help identify strategies for lake restoration. Photosynthetic rates and concentrations of inorganic nutrients and phytoplankton pigments were measured over a period of one year. More than 90% of the chlorophyll a (chl a) in the lake was accounted for by Chlorophyceae, Cyanophyceae, and Bacillariophyceae. Concentrations of chl a, which averaged 75 µg L −1 , fluctuated weekly during dry weather by as much as a factor of four. Phytoplankton growth rates were about 30% higher 1-2 days after rain events than after periods of dry weather, the implication being that allochthonous nutrient loading has a significant effect on the dynamics of the phytoplankton community in the lake. Therefore, dredging of sediments will likely produce no long-term improvement in water quality. More than 100 storm drains currently discharge into the lake, and diversion of those drains may be the most cost-effective strategy for effecting a long-term improvement in water quality.

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“…In reality, maximum C-fixation, maximum N-or Passimilation, and cell doubling rates are highly variable. This requires at least cellular C, N, and Chl a to be explicitly resolved, (linking for example intracellular nutrient allocation to photo-acclimation Shuter, 1979;Laws et al, 1983;Pahlow, 2005;Armstrong, 2006).…”

Section: Differences Between Maximum Carbon Fixation and Maximum Growmentioning

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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A theoretical and experimental examination of the predictions of two recent models of phytoplankton growth

Cited by 26 publications

References 18 publications

Carbon dioxide regulation of nitrogen and phosphorus in four species of marine phytoplankton

Carbon dioxide regulation of nitrogen and phosphorus in four species of marine phytoplankton

Nutrients and Phytoplankton in a Shallow, Hypereutrophic Urban Lake: Prospects for Restoration

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

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