Numerical Simulation of phytoplankton productivity in partially mixed estuaries

Peterson, David H.; Festa, John F.

doi:10.1016/0272-7714(84)90016-7

Cited by 54 publications

(27 citation statements)

References 67 publications

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“…The weaknesses of Chl a as a biomass predictor have often been emphasised (see review of C:Chl a ratios by Peterson and Festa 1984). The first interesting result from our study is that it supports this view that Chl a is a poor indicator of biomass.…”

Section: Resultssupporting

confidence: 78%

Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species

et al. 2004

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Phytoplankton microscopic enumerations and HPLC analyses of their pigments were performed weekly for a complete year at a coastal station in the English Channel. The taxonomic composition of the phytoplankton community was assessed using the HPLC results combined with the mathematical tool CHEMTAX in two different ways. Firstly, without using the species level taxonomic information obtained at the microscopic level (blind analyses), and secondly by including the information from the microscopic taxonomic analysis (directed analyses). The results indicate that, due to the particular pigment composition of some species (for example, the dinoflagellate, Karenia mikimotoi and the haptophyte, Phaeocystis pouchetii), a blind analysis would result in very significant errors in the taxonomic determination of the bloom events at this station. Major blooms of Karenia mikimotoi and P. pouchetii were mistaken for blooms of diatoms on the basis of a blind HPLC-CHEMTAX analysis. Only with the information from the microscopic observations was it possible to obtain an accurate representation of the phytoplankton community.

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Section: Resultssupporting

confidence: 78%

Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species

et al. 2004

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“…pb,,,). Instead R, may covary with growth rate (Peterson and Festa 1984). When we computed R, based on allometric relationships between size and metabolic rate (Robinson et al 1983), the values were up to lo-fold higher than the equivalent of 5% of Pbmax.…”

Section: Discussionmentioning

confidence: 99%

“…The ratio of the photic zone to the mixed zone (2, : 2,) is probably a major determinant of planktonic primary production in the Hudson, as it is in other nutrientsufficient systems (Peterson and Festa 1984). If we take the average for the entire tidal freshwater portion in total, Zp : 2, would be -0.05 in winter and -0.2 in summer.…”

Section: Discussionmentioning

confidence: 99%

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Can phytoplankton maintaina positive carbon balance in a turbid, freshwater, tidal estuary?

Cole¹,

Caraco²,

Peierls³

1992

Limnology & Oceanography

217

151

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An analysis of phytoplankton primary production in the tidal freshwater portion of the Hudson River estuary suggests that net primary production is strongly limited by light and mixing regime. In this turbid, well-mixed system, cells spend from 18 to 22 h d-l below the 1% light level. Autotrophic dark respiration, conservatively estimated at 5% of pb,,,, is of sufficient magnitude to make positive algal growth impossible over much of the river and much of the year. It is particularly difficult to explain the observed increase in algal biomass during blooms in spring and summer. We hypothesize that such blooms can occur only in a small fraction of the river where depth is 54 m.

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“…Physical processes including advection and diffusion play an important role in estuarine plankton population dynamics (Haas et al 1981, Delgadillo-Hinojosa et al 1997, Shen et al 1999. In this context, incorporation of physical processes is essential in estuarine ecosystem process-modeling and the relationship between physical processes and plankton population-dynamics in coastal estuarine systems have received increasing attention (Peterson & Festa 1984, Cloern 1991, Eldridge & Sieracki 1993, Vidergar et al 1993.…”

Section: Introductionmentioning

confidence: 99%

Ecosystem modeling analysis of size-structured phytoplankton dynamics in the York River estuary, Virginia (USA). I. Development of a plankton ecosystem model with explicit feedback controls and hydrodynamics

Sin¹,

Wetzel²

2002

Mar. Ecol. Prog. Ser.

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An ecosystem simulation model was developed to investigate potential mechanisms controlling the size-structured phytoplankton and nutrient dynamics in the mesohaline zone of the York River estuary. The model included 12 state variables in a unit volume (m 3 ) describing the distribution of carbon and nutrients (nitrogen, phosphorus) in the surface mixed layer. General size-scale relationships and density-dependent feedback control terms were used in the ecosystem model. Forcing functions included incident solar radiation, water temperature, wind stress, river flow and tide, which include advective transport and turbulent mixing. Advective transport and turbulent mixing were incorporated into the model explicitly without coupling to other hydrodynamic models. The ecosystem model was developed in Fortran90 using differential equations that were solved numerically using the fourth-order Runge-Kutta (explicit ) technique. After calibrating the ecosystem model, forcing and state variables in the model were validated using pre-existing data and field data collected over an annual cycle. Model predictions for forcing and state variables generally followed the pattern of field observations and were within the range of field data. Model sensitivity analysis was also performed to examine how sensitive model output was to specified changes in parameter values. Model output was not sensitive to changes in most parameters, suggesting that the model is relatively robust. These results suggested that the model including explicit feedback controls and hydrodynamic processes captures plankton/nutrient dynamics and can be used for additional modeling analyses of phytoplankton and nutrient dynamics in the York River estuary, Virginia. KEY WORDS: Ecosystem model · Hydrodynamic processes · Feedback controls · York River estuaryResale or republication not permitted without written consent of the publisher

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Numerical Simulation of phytoplankton productivity in partially mixed estuaries

Cited by 54 publications

References 67 publications

Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species

Using HPLC pigment analysis to investigate phytoplankton taxonomy: the importance of knowing your species

Can phytoplankton maintaina positive carbon balance in a turbid, freshwater, tidal estuary?

Ecosystem modeling analysis of size-structured phytoplankton dynamics in the York River estuary, Virginia (USA). I. Development of a plankton ecosystem model with explicit feedback controls and hydrodynamics

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