Abstract

With the eutrophication of many freshwaters and coastal environments, phytoplankton blooms have become a common phenomenon. This article uses a reaction‐diffusion model to investigate the implications of mixing processes for the dynamics and species composition of phytoplankton blooms. The model identifies four key parameters for bloom development: incident light intensity, background turbidity, water column depth, and turbulent mixing rates. The model predicts that the turbulent mixing rate is a major determinant of the species composition of phytoplankton blooms. In well‐mixed environments, the species with lowest “critical light intensity” should become dominant. But at low mixing rates, the species with lowest critical light intensity is displaced if other species obtain a better position in the light gradient. Instead of a gradual change in species composition, the model predicts steep transitions between the dominance regions of the various species. The model predicts a low species diversity: phytoplankton blooms in eutrophic environments should be dominated by one or a few species only. The model predictions are consistent with laboratory competition experiments and many existing field data. We recommend examining competition in phytoplankton blooms under well‐controlled laboratory conditions, and we derive scaling rules that facilitate translation from the laboratory to the field.

Highlights

  • Maximum bloom size per unit surface area depends on the key parameters of incident light intensity

  • Large blooms per unit surface area can develop if the incident light intensity is high

  • that mixing processes and competition for light should have a profound impact on the dynamics and species composition of phytoplankton blooms

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Summary

Population Dynamics

We consider a water column with a cross section of one unit area and with n phytoplankton species. Let qi(s, t) denote the population density (numbers per unit volume) of a phytoplankton species i at depth s and time t. The change of flux with depth, ѨJi/Ѩs, gives the net change of the phytoplankton population density caused by local transport processes. We assume that the specific production rate of a species i, pi(I(s, t)), is an increasing and possibly saturating function of light intensity. The net flux of a phytoplankton species i at depth s is proportional to its local population density gradient: Ji(s, t). We assume that the water column is closed, with no phytoplankton cells entering or leaving the column at the top or the bottom This gives the boundary conditions: Combining equations (1)–(5), the population size per unit surface area of a species i changes with time according to z. Where the flux terms cancel out because the boundaries are closed

Light Gradient
Coexistence and Competitive Exclusion
Illustration of a Model Run
Vertical Distribution Patterns
Scaling Rules
Complete Mixing
Zcr ϭ
Incomplete Mixing and Differential Diffusion Rates
Incomplete Mixing in Different Environments
Incomplete Mixing and Incident Light Intensity
Incomplete Mixing and Background Turbidity
Incomplete Mixing and Water Column Depth
Summary
Discussion
Literature Cited
Full Text
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