Phytoplankton biomass and primary production dynamics in Lake Kariba

2012 ◽  
Vol 17 (4) ◽  
pp. 275-289 ◽  
Author(s):  
Ndebele-Murisa M. Regina ◽  
Musil C. Frank ◽  
Raitt L. Miles
Keyword(s):  
Primary Production ◽  
Phytoplankton Biomass ◽  
Lake Kariba ◽  
Production Dynamics

In Vivo Fluorescence of Chlorophyll A: Estimation of Phytoplankton Biomass and Activity in Římov Reservoir (Czech Republic)

1993 ◽  
Vol 28 (6) ◽  
pp. 29-33 ◽  
Author(s):  
V. Vyhnálek ◽  
Z. Fišar ◽  
A. Fišarová ◽  
J. Komárková
Keyword(s):  
Czech Republic ◽  
Chlorophyll Fluorescence ◽  
Primary Production ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
In Vivo Fluorescence ◽  
Fluorescence Of Chlorophyll A ◽  
Římov Reservoir ◽  
Natural Phytoplankton

The in vivo fluorescence of chlorophyll a was measured in samples of natural phytoplankton taken from the Římov Reservoir (Czech Republic) during the years 1987 and 1988. The fluorescence intensities of samples either with or without addition of 3-(3,4-dichlorophenyl)-1,1-dimethylurea (diuron, DCMU) were found reliable for calculating the concentration of chlorophyll a during periods when cyanobacteria were not abundant. The correction for background non-chlorophyll fluorescence appeared to be essential. No distinct correlation between a DCMU-induced increase of the fluorescence and primary production of phytoplankton was found.

scholarly journals Seasonal patterns in phytoplankton biomass across the northern and deep Gulf of Mexico: a numerical model study

2018 ◽  
Vol 15 (11) ◽  
pp. 3561-3576 ◽  
Author(s):  
Fabian A. Gomez ◽  
Sang-Ki Lee ◽  
Yanyun Liu ◽  
Frank J. Hernandez Jr. ◽  
Frank E. Muller-Karger ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Primary Production ◽  
Phytoplankton Biomass ◽  
Mississippi Delta ◽  
Three Dimensional ◽  
Rate Of Change ◽  
Integrated Analysis ◽  
Biogeochemical Model ◽  
Seasonal Patterns ◽  
Biogeochemical Models

Abstract. Biogeochemical models that simulate realistic lower-trophic-level dynamics, including the representation of main phytoplankton and zooplankton functional groups, are valuable tools for improving our understanding of natural and anthropogenic disturbances in marine ecosystems. Previous three-dimensional biogeochemical modeling studies in the northern and deep Gulf of Mexico (GoM) have used only one phytoplankton and one zooplankton type. To advance our modeling capability of the GoM ecosystem and to investigate the dominant spatial and seasonal patterns of phytoplankton biomass, we configured a 13-component biogeochemical model that explicitly represents nanophytoplankton, diatoms, micro-, and mesozooplankton. Our model outputs compare reasonably well with observed patterns in chlorophyll, primary production, and nutrients over the Louisiana–Texas shelf and deep GoM region. Our model suggests silica limitation of diatom growth in the deep GoM during winter and near the Mississippi delta during spring. Model nanophytoplankton growth is weakly nutrient limited in the Mississippi delta year-round and strongly nutrient limited in the deep GoM during summer. Our examination of primary production and net phytoplankton growth from the model indicates that the biomass losses, mainly due to zooplankton grazing, play an important role in modulating the simulated seasonal biomass patterns of nanophytoplankton and diatoms. Our analysis further shows that the dominant physical process influencing the local rate of change of model phytoplankton is horizontal advection in the northern shelf and vertical mixing in the deep GoM. This study highlights the need for an integrated analysis of biologically and physically driven biomass fluxes to better understand phytoplankton biomass phenologies in the GoM.

Relationships of Phytoplankton Biomass with Soluble Nutrients, Primary Production, and Chlorophyll a in Lake Erie, 1970

1976 ◽  
Vol 33 (3) ◽  
pp. 601-611 ◽  
Author(s):  
M. Munawar ◽  
N. M. Burns
Keyword(s):  
Primary Production ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Lake Erie ◽  
Soluble Reactive Phosphorus ◽  
Distribution Patterns ◽  
Statistical Procedure ◽  
Annual Average ◽  
Average Distribution ◽  
Reactive Phosphorus

Comparison of the annual average distribution patterns of phytoplankton biomass, chlorophyll a, primary production, soluble reactive phosphorus, nitrate + nitrite, and ammonia concentrations revealed that these six variables had very similar distributions in Lake Erie during 1970. However, statistical analysis of the data only revealed a few consistent relationships between these variables. The phytoplankton biomass was correlated with chlorophyll a only in the summer and fall as was primary production with chlorophyll a and biomass. There was no correlation between these three variables during the spring. Also, there was no consistent relationship between biomass and soluble nutrients. The primary production and activity coefficient (mg Cassimilated per milligram phytoplankton biomass per day) were found to be unrelated to temperature. The statistical procedure of factor analysis showed that in the spring, primary production correlated with the phosphorus and nitrogen soluble nutrients only, whereas during summer, primary production correlated with biomass, chlorophyll a, the major plankton groups (Cyanophyta, Chlorophyta, Chrysomonadinae, and Diatomeae), and the phosphorus nutrients. In the fall, production was positively correlated with phytoplankton biomass and with the Chlorophyta in particular. The use of chlorophyll a and temperature as variables in the equation to estimate phytoplankton growth in Lake Erie was found to be questionable.

scholarly journals Biogeochemical versus ecological consequences of modeled ocean physics

2017 ◽  
Vol 14 (11) ◽  
pp. 2877-2889 ◽  
Author(s):  
Sophie Clayton ◽  
Stephanie Dutkiewicz ◽  
Oliver Jahn ◽  
Christopher Hill ◽  
Patrick Heimbach ◽  
...  
Keyword(s):  
Primary Production ◽  
Ocean Circulation ◽  
Mixed Layer ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
Resource Competition ◽  
Nutrient Supply ◽  
Zooplankton Biomass ◽  
Global Ocean ◽  
Competition Theory

Abstract. We present a systematic study of the differences generated by coupling the same ecological–biogeochemical model to a 1°, coarse-resolution, and 1∕6°, eddy-permitting, global ocean circulation model to (a) biogeochemistry (e.g., primary production) and (b) phytoplankton community structure. Surprisingly, we find that the modeled phytoplankton community is largely unchanged, with the same phenotypes dominating in both cases. Conversely, there are large regional and seasonal variations in primary production, phytoplankton and zooplankton biomass. In the subtropics, mixed layer depths (MLDs) are, on average, deeper in the eddy-permitting model, resulting in higher nutrient supply driving increases in primary production and phytoplankton biomass. In the higher latitudes, differences in winter mixed layer depths, the timing of the onset of the spring bloom and vertical nutrient supply result in lower primary production in the eddy-permitting model. Counterintuitively, this does not drive a decrease in phytoplankton biomass but results in lower zooplankton biomass. We explain these similarities and differences in the model using the framework of resource competition theory, and find that they are the consequence of changes in the regional and seasonal nutrient supply and light environment, mediated by differences in the modeled mixed layer depths. Although previous work has suggested that complex models may respond chaotically and unpredictably to changes in forcing, we find that our model responds in a predictable way to different ocean circulation forcing, despite its complexity. The use of frameworks, such as resource competition theory, provides a tractable way to explore the differences and similarities that occur. As this model has many similarities to other widely used biogeochemical models that also resolve multiple phytoplankton phenotypes, this study provides important insights into how the results of running these models under different physical conditions might be more easily understood.

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