Plankton bloom

From Coastal Wiki
Jump to: navigation, search

It is recommended to read this article in conjunction with the article Nutrient conversion in the marine environment.


Fig. 1. Envisat MERIS true color image of a phytoplankton bloom in the Barents Sea. The bright blue colour is characteristic of the coccolithophore species Emiliana huxleyi.

This article focuses on phytoplankton blooms, rapid increases in populations of photosynthetic plankton such as microalgae and cyanobacteria (see Marine Plankton). A plankton bloom is a short-lived event in which plankton abundance increases by orders of magnitude before declining as environmental conditions change. Quantitative definitions of blooms based on cell density, biomass, or chlorophyll-a (Chl-a) concentration depend on the plankton species and local environmental conditions.

Phytoplankton blooms occur worldwide when temperature, light, and nutrient conditions favor rapid growth. In many coastal waters, eutrophication has increased the frequency, intensity, or duration of phytoplankton blooms. However, bloom development also depends on factors such as local light climate, flushing, stratification, and grazing pressure. These controlling factors can be altered by human activities and climate change. This article introduces several basic concepts underlying plankton bloom dynamics. A highly simplified model of bloom dynamics is presented in Appendix A.


Phytoplankton

Phytoplankton are aquatic organisms that grow through the uptake of CO2 and dissolved mineral nutrients by photosynthesis. This photosynthetic uptake of CO2 is called primary production because it generates the organic matter that forms the basis of marine food webs. The organic matter produced by gross photosynthesis minus respiration is called net primary production.

Thousands of phytoplankton species are known (Sournia et al., 1991[1]; Andersen, 1992[2]). The dominant groups in seawater include diatoms, flagellates, and cyanobacteria. Small phytoplankton species (picophytoplankton, size < 2 micron, consisting mainly of cyanobacteria) generally have high nutrient-uptake efficiency and therefore dominate nutrient-poor ocean waters. Larger phytoplankton such as diatoms often dominate biomass in nutrient-rich waters (Edwards et al., 2011[3]; Burson et al., 2018[4]). Blooms can extend over large areas, as illustrated in Fig. 1.

All phytoplankton species assimilate dissolved CO2 under the influence of light and release oxygen through photosynthesis. A commonly used stoichiometric representation of marine primary production, based on the average elemental composition of oceanic phytoplankton, is (Redfield, 1958[5])

[math]106 \, CO_2 + 16 \, HNO_3 + H_3PO_4 + 122 \, H_2 O + \mathrm{trace} \; \mathrm{elements} \; \mathrm{and} \; \mathrm{vitamins} + \mathrm{light} \rightarrow C_{106}H_{263}O_{110}N_{16}P + 138 \, O_2 . \qquad (1)[/math]

Marine phytoplankton are responsible for approximately half of global oxygen production (Field et al., 1998[6]). Phytoplankton species are called 'autotrophs' or 'primary producers' because they produce organic matter from inorganic substances. They constitute an important food source for other marine organisms, especially zooplankton. However, the distinction between phytoplankton and zooplankton is not always clear because many plankton species are mixotrophic: they are capable of photosynthesis while also ingesting dissolved or particulate organic matter (Flynn et al., 2013[7]). For more details on plankton groups, see the article Marine Plankton.

The size of a plankton bloom is commonly expressed in terms of organic carbon biomass ([math]g \, C[/math]). Biomass can be estimated from ash-free dry weight measurements (see In situ monitoring of eutrophication, Sampling tools for the marine environment). More commonly, phytoplankton biomass is estimated from chlorophyll-a concentrations, since chlorophyll-a is present in all phytoplankton groups and can be measured using optical techniques based on its fluorescence properties. Relevant techniques are described in the articles Plankton remote sensing and Differentiation of major algal groups by optical absorption signatures. A limitation of remote sensing is that it primarily detects surface blooms, whereas subsurface blooms may also occur (Blondeau-Patissier et al., 2014[8]).

Conditions for growth: light, temperature and nutrients

Besides CO2 and sunlight, phytoplankton require nutrients for growth. The most important are nitrogen (N), phosphorus (P), and, for diatoms, silicon (Si), mainly in the form of dissolved nitrate, ammonium, phosphate, and silicate (see Nutrient conversion in the marine environment). Small amounts of trace metals (Fe, Mn, Zn, Cu, Ni, etc.) and certain vitamins are also required. Limitation of any essential nutrient can restrict phytoplankton growth. Because nutrient concentrations in much of the ocean are low, phytoplankton species compete intensely for available resources.

The average elemental ratio in marine phytoplankton biomass is often approximated by the Redfield ratio of 106 C : 16 N : 1 P, corresponding to Eq. (1). However, nutrient requirements vary among species and environmental conditions (Davidson et al., 2012[9]). Consequently, some species are favored over others under specific nutrient regimes. Growth also depends strongly on light and temperature (Bissinger et al., 2008[10]; Boyd et al., 2013[11]). Optimal conditions differ among phytoplankton species.

Fig. 2. Multi-annual observations of the diatom Chaetoceros socialis (red spikes) at a measuring station in the North Sea, 10 km north of the Dutch Wadden island of Terschelling. Simultaneous observations of dissolved nitrate concentration (black) and surface salinity (blue) are also shown. Bloom peaks generally coincide with rapid nutrient depletion and relatively low salinity. From Wagner-Cremer et al. (2018)[12].

Under favorable conditions, phytoplankton populations can grow rapidly as long as sufficient nutrients and light are available. Population growth occurs through cell division, in which one parent cell produces two or more daughter cells. Division rates vary among species and depend strongly on temperature and light conditions. Under optimal conditions, some species may divide within hours, whereas others require several days. Because growth occurs simultaneously throughout the population, phytoplankton blooms often show exponential growth.

The short-lived and explosive nature of blooms is illustrated in Fig. 2, which shows a multi-year record of the diatom Chaetoceros socialis in the North Sea near Terschelling. The temporal relationship with dissolved nitrate concentration and salinity is discussed in the Appendix.

The conditions controlling phytoplankton blooms vary seasonally. In temperate regions, winter mixing replenishes nutrients in surface waters by transporting nutrient-rich deep water upward. As light intensity and temperature increase in spring, conditions become favorable for rapid phytoplankton growth, and large blooms often develop. Growth is generally strongest in the upper illuminated layer of the ocean, the euphotic zone. Strong vertical mixing can inhibit bloom formation because phytoplankton are transported into deeper, darker water layers where photosynthesis is reduced.

In many ocean regions, blooms begin when wintertime convective mixing weakens and the upper ocean becomes stratified[13]. Stratification develops when warmer and/or fresher surface water overlies colder or saltier deeper water. Organic detritus often accumulates near the density interface (pycnocline), where microbial mineralization releases nutrients. These conditions can favor the development of intense blooms, including blooms of mixotrophic plankton species that combine photosynthesis with uptake of organic matter (Berdalet et al., 2014[14]; Sigman and Hain, 2012[15]). In the open ocean, chlorophyll concentrations are often highest near the base of the euphotic zone, forming a subsurface chlorophyll maximum, commonly located tens to around 100 m below the surface (see Open ocean habitat).

Projected future ocean changes, including increasing temperature, stronger stratification, eutrophication, and ocean acidification, may favor smaller and mixotrophic phytoplankton species while disadvantaging some larger diatoms (Glibert, 2020[16]). Observations further indicate that increasing seawater temperature is often associated with reduced phytoplankton biomass and productivity, together with shifts from larger phytoplankton groups toward smaller species such as cyanobacteria (Van de Poll et al., 2013[17]).

Other factors that condition plankton blooms

Inter-species competition

The environmental conditions required for optimal growth differ among plankton species. Because resources such as nutrients, light, and temperature vary strongly in space and time, a great diversity of phytoplankton species with different ecological adaptations has evolved (Hutchinson, 1961[18]). Species that use the available resources most efficiently under prevailing conditions generally achieve the highest growth rates and may temporarily dominate the phytoplankton community. Consequently, plankton bloom dynamics cannot be described adequately using only average growth characteristics across species.

A common assumption in ecosystem models is that phytoplankton community composition adapts rapidly to prevailing environmental conditions, thereby maximizing the use of available resources (Glibert, 2017[19]). Field observations support this view. For example, studies in the eutrophic Wadden Sea and the tropical Godavari estuary showed that phytoplankton composition changes with nutrient availability and nutrient ratios (Philippart et al., 2007[20]; Bharathia et al., 2018[21]). In these systems, relatively low N:P ratios favored large phytoplankton such as diatoms, whereas high N:P ratios promoted smaller flagellates and cyanobacteria.

Phytoplankton composition also depends on other environmental factors. Diatoms require dissolved silicate and generally have lower temperature optima than many cyanobacteria and flagellates (Anderson, 2000[22]). The chemical form of nitrogen is also important[19]. Many diatoms and dinoflagellates preferentially use nitrate (NO3-), whereas small flagellates and picocyanobacteria often grow efficiently on ammonium (NH4+) derived from the decomposition of organic matter. Diatoms typically achieve high growth rates in nutrient-rich turbulent waters, while small phytoplankton species are more competitive under nutrient-poor and low-light conditions.

Coccolithophores, such as Emiliania huxleyi, frequently dominate oligotrophic subtropical surface waters. Although the production of calcium carbonate plates (coccoliths) requires substantial energy, these species can remain competitive because of highly efficient nutrient acquisition[23].

Competition among phytoplankton species is also influenced by mobility. Certain plankton species can migrate vertically to exploit favorable light and nutrient conditions. For example, many dinoflagellates migrate upward during the day for photosynthesis and downward at night to access deeper nutrient-rich waters. In low-turbulence environments, small motile phytoplankton such as flagellates can outcompete larger non-motile species such as diatoms, which would otherwise sink below the photic zone. Consequently, flagellates often dominate stratified waters with weak turbulence (Margalef, 1978[24]; Klausmeier and Litchman, 2001[25]).

Flagella and efficient nitrogen uptake are special adaptations that enable dinoflagellates to dominate in nutrient-poor, low-turbulence waters, as 'specialist' or 'K-strategy' species. This contrasts with fast-growing ('r-strategy') diatoms that thrive in nutrient-rich turbulent waters (Glibert, 2016[23], see also r/K selection theory). In nutrient-poor, sunlit surface waters, typical for the subtropical ocean, some microbial species (e.g. the cyanobacterium Trichodesmium) are capable of fixing dissolved nitrogen gas N2. This crucial adaptation, called diazotrophy, is a major driver of the primary productivity in oligotrophic oceans.

Some ecosystem models simulate phytoplankton community composition by assuming that species composition adjusts continuously to maximize net primary production under the prevailing light, nutrient, and temperature conditions. Resource consumption by the phytoplankton community alters the local environment through nutrient depletion and self-shading, thereby influencing the optimal species composition at subsequent time steps. Simulations based on this approach often reproduce observed bloom dynamics reasonably well (Los and Wijsman, 2007[26]).

An additional complexity is that phytoplankton can adjust their physiological investment in light harvesting and nutrient uptake. For example, cells may allocate more energy to pigment production under low-light conditions, at the expense of nutrient uptake efficiency, or vice versa. Such trade-offs allow phytoplankton to optimize growth at specific depths where the balance between light and nutrient availability is most favorable. This mechanism has been proposed to explain the subsurface chlorophyll maximum commonly observed in the open ocean (Pahlow, 2005[27]).

Predation

Phytoplankton populations are limited not only by nutrient availability but also by mortality and grazing. Phytoplankton constitute a major food source for marine organisms, especially zooplankton and mixotrophic protists. The smallest grazers, collectively referred to as microzooplankton, include flagellates, ciliates, and mixotrophic plankton. These organisms can respond rapidly to fluctuations in prey abundance and therefore exert strong control over populations of picophytoplankton.

Larger grazers, such as copepods in the open ocean and filter-feeding benthic organisms in estuaries, consume large phytoplankton species such as diatoms and dinoflagellates. However, because these grazers generally respond more slowly to rapid phytoplankton growth, blooms of large phytoplankton species are often less tightly controlled by grazing[20]. Reduced grazing pressure on some dinoflagellates may also result from escape behavior (Harvey and Menden-Deuer, 2012[28]) or from lower nutritional quality relative to smaller phytoplankton cells (Branco et al., 2020[29]).

Digestion of phytoplankton by large zooplankton species produces fecal pellets and organic aggregates that contribute to the downward export of carbon and nutrients. A substantial fraction of this material is remineralized during sinking or after deposition on the seabed. In shallow coastal waters, mineralization of organic matter in bottom sediments releases nutrients that can subsequently fuel new phytoplankton production in the water column. Nutrient recycling is therefore generally more rapid in coastal systems than in the open ocean, where sinking organic matter can be transported to great depths.

Mortality

Phytoplankton mortality is caused by several processes, including viral infection, grazing, physiological stress, and sinking losses. Marine viruses are thought to play a major role in phytoplankton mortality (Baudoux, 2007[30]). In subtropical and tropical oceans, virus-induced mortality can equal or exceed grazing losses, whereas grazing generally dominates at higher latitudes (Mojica et al., 2016[31]).

Most dead phytoplankton cells are too small to sink individually. Part of the dead biomass is decomposed by bacteria in the euphotic zone, releasing nutrients that can support new phytoplankton growth. Another fraction aggregates with other organic and inorganic particles to form larger sinking particles known as marine snow. In the deep ocean, part of this sinking organic matter contributes to long-term carbon sequestration (see Ocean carbon sink).

In addition to gravitational sinking, downward transport of organic matter also occurs through downwelling circulation and episodic subduction events (see Shelf sea exchange with the ocean and Ekman transport) (Llort et al., 2018[32]). Together, these biological and physical processes contribute substantially to the oceanic uptake of atmospheric CO2. The global ocean carbon sink currently absorbs roughly one quarter of anthropogenic CO2 emissions (DeVries et al., 2019[33]).

Consequences of plankton blooms

Increased fishery yields

Comparative studies of estuaries and coastal seas indicate that moderate nutrient enrichment can increase primary production and thereby enhance secondary production and fishery yields, although excessive eutrophication may ultimately reduce ecosystem quality through hypoxia and habitat degradation (Nixon, 1988[34]; Nixon and Buckley, 2002[35]).

Oxygen depletion

Phytoplankton blooms produce oxygen through photosynthesis. During bloom decay, however, microbial decomposition of organic matter consumes dissolved oxygen. The oxygen demand associated with decomposition of a large bloom can be substantial (see Possible consequences of eutrophication). If oxygen consumption exceeds replenishment by mixing, flushing, or atmospheric exchange, hypoxia or anoxia may develop.

Oxygen depletion is common in stratified lakes, enclosed seas, lagoons, and estuaries. Stratification caused by temperature or salinity differences suppresses vertical mixing between oxygen-rich surface waters and deeper layers. At the same time, sinking organic matter from plankton blooms is decomposed in bottom waters, further increasing oxygen consumption (see Estuarine turbidity maximum). Severe hypoxia or anoxia can lead to mortality of fish, benthic organisms, and other aerobic species. In many coastal systems, oxygen depletion is an indirect consequence of eutrophication-driven phytoplankton blooms.

Harmful algae

A relatively small fraction of phytoplankton species produces toxins or other harmful compounds and is therefore classified as harmful algae. Harmful algal blooms (HABs) occur naturally and share many ecological characteristics with non-toxic phytoplankton blooms (Smayda, 1997[36]). Toxic algae can affect marine food webs and human health because toxins may accumulate in fish and shellfish consumed by humans, see Harmful algal blooms.

Toxic compounds produced by certain diatoms, dinoflagellates, and cyanobacteria are thought to function partly as defense mechanisms against grazers or competitors (see Chemical ecology and phytoplankton and Functional metabolites in phytoplankton). However, the effectiveness of these compounds in reducing grazing pressure remains debated (Zingone and Wyatt, 2004[37]).

Harmful algal blooms (HABs) are a natural and frequent phenomenon, similar to non-harmful plankton blooms (Smayda, 1997[38]). Evidence suggests that the frequency, geographic distribution, and impacts of some HABs have increased during recent decades (Hallegraeff, 1993[39]). Proposed drivers include eutrophication, altered nutrient ratios, increased stratification, ocean warming, and ocean acidification (Glibert, 2020[16]). However, the relative importance of these factors remains uncertain because HAB dynamics are highly species- and region-specific (Anderson, 2012[40]; Berdalet et al., 2016[41]; Wells et al., 2020[42]).

Occurrence of plankton blooms

Fig. 3. Mean global distribution of ocean surface chlorophyll-a derived from SeaWiFS satellite observations. High concentrations occur in coastal upwelling zones, high-latitude oceans, the Arctic region, and eutrophic coastal waters. Subsurface chlorophyll maxima in oligotrophic oceans are not visible. Image credit: NASA.

The global distribution of surface chlorophyll-a, shown in Fig. 3, is commonly used as an indicator of phytoplankton biomass and net primary production. Phytoplankton blooms require sufficient light and nutrient availability. Naturally nutrient-rich surface waters occur in oceanic upwelling zones, where deep nutrient-rich water rises toward the surface (see Ekman transport and Shelf sea exchange with the ocean). Major upwelling systems occur along the Atlantic coast of Africa and the Pacific coasts of California and South America, where wind-driven circulation and Earth’s rotation transport surface water offshore. In these regions, net primary production (NPP) may exceed 1000 [math]g \, C \, m^{-2} \, y^{-1}[/math], compared with a global ocean average of roughly 150 [math]g \, C \, m^{-2} \, y^{-1}[/math][43]. In contrast, subtropical ocean zones generally exhibit low primary production because downwelling and strong stratification limit the upward transport of nutrients into the photic zone (see Open ocean habitat).

High nutrient concentrations may occur in coastal waters, especially in estuaries and lagoons, largely due to nutrient inputs associated with human activities (see Eutrophication in coastal environments) (Howarth, 1988[44]). In these systems, much of the annual primary production occurs during seasonal or episodic plankton blooms. Nevertheless, annual NPP in coastal ecosystems is not always exceptionally high; typical values range from 50 to 400 [math]g \, C \, m^{-2} \, y^{-1}[/math], and values above 500 [math]g \, C \, m^{-2} \, y^{-1}[/math] are relatively uncommon (Cloern et al., 2014[45]). One important reason is that phytoplankton growth in estuaries is often limited more strongly by light availability than by nutrient supply. Suspended sediments and dissolved organic matter increase turbidity and reduce light penetration, particularly in deep or dredged estuaries (see Estuarine turbidity maximum and Which resource limits coastal phytoplankton growth/ abundance: underwater light or nutrients?). Model studies indicate that in well-mixed turbid estuaries, plankton blooms are generally restricted to regions downstream of the estuarine turbidity maximum (Liu et al., 2018[46]).

The influence of river discharge on estuarine phytoplankton blooms is complex. High discharge increases nutrient supply but can also shorten water residence times and flush phytoplankton out of the estuary. In some systems, however, strong river discharge enhances stratification and improves light conditions in the surface layer, thereby favoring bloom development (McSweeney et al., 2016[47]). In estuaries with extensive intertidal flats, pelagic phytoplankton compete with benthic microalgae for nutrients (De Jonge and van Beusekom, 1992[48]). Comparisons among estuaries indicate that total ecosystem respiration often exceeds gross primary production, implying that many estuaries are net heterotrophic systems and net sources of CO2 to the atmosphere (Caffrey, 2004[49]; Gattuso et al., 1998[50]).


Appendix A: Simplified plankton bloom model

The occurrence of plankton blooms, with rapid emergence and equally rapid disappearance, is a prominent feature of eutrophication. This appendix provides a qualitative explanation of plankton bloom dynamics, following the paper by Huppert et al. (2002[51]). The explanation is based on a simple model in which a plankton bloom is related to the available amount of a certain nutrient. This nutrient is assumed to be essential for the growth of the plankton population, but to be present in such a low concentration that it limits the growth of the population. Because the uptake of the nutrient by the plankton decreases the nutrient concentration, the population growth and the nutrient concentration form together a self-regulating feedback system. The different processes that play a role are set out below. The model is meant to enable a better understanding of the plankton bloom process; it is too simple for application to real field situations because many of the processes discussed previously are ignored. A particularly important missing feature is the coupled dynamics of grazing zooplankton that curbs phytoplankton growth and thus delays nutrient depletion.

We call [math]P(t)[/math] the biomass, representing the size of the plankton population at time [math]t[/math] in a certain volume [math]V[/math] of the sea, and [math]N(t)[/math] the average nutrient concentration in this volume. The temporal variation of the plankton biomass, [math]dP/dt[/math] and the nutrient concentration, [math]dN/dt[/math], are regulated according to the following assumptions and approximations:

  • Nutrient uptake [math]B[/math]. The uptake depends on the plankton biomass [math]P[/math] and on the nutrient concentration [math]N[/math]. If the nutrient concentration is low (limiting), linearity of the dependence in both [math]P[/math] and [math]N[/math] is a reasonable assumption, [math]B=\beta (t) \, P \, N[/math]. The uptake efficiency [math]\beta(t)[/math] depends on other environmental conditions for plankton growth, in particular temperature and light. Nutrient uptake increases the plankton biomass by [math]B_P=\beta_P(t) \, P \, N[/math] and decreases the nutrient concentration by [math]B_N=-\beta_N(t) \, P \, N[/math]. The ratio [math]\rho=\beta_N(t)/\beta_P(t)[/math] is constant. Saturation of the nutrient uptake (i.e. [math]B[/math] independent of [math]N[/math] when [math]N[/math] is abundant) is ignored.
  • Biomass decay with nutrient restitution [math]G[/math]. The plankton biomass decreases by respiration, mortality and mineralization of plankton detritus, [math]G_P = - \gamma (t) \, P[/math], while the nutrient concentration increases with restitution of nutrient by [math]G_N=\rho \gamma (t) \, P[/math]. It is assumed that respiration and mineralization are quasi-instantaneous processes; their efficiency for nutrient restitution is expressed by the rate factor [math]\gamma(t)[/math].
  • Nutrient loss [math]S[/math] related to the loss of plankton biomass by predation, sinking to the bottom and export, and therefore assumed proportional to the plankton biomass: [math]S=- \sigma (t) \, P[/math]. Predation by zooplankton depends not only on the plankton biomass but also on the biomass of zooplankton. The zooplankton population itself depends on the plankton biomass and increases when the plankton biomass is high. However, zooplankton dynamics is ignored in the model.
  • Nutrient loss [math]A=-\alpha (t) \, N[/math], as a result of biogeochemical processes that limit the availability of nutrients for uptake by plankton.
  • Nutrient supply from external sources, [math]Q[/math]. Major external nutrient supplies (partly in the form of organic material) are coming from rivers, atmospheric deposition and stirring up of organic bed material.

We focus on the period in which the plankton population experiences rapid growth and rapid decline. This period is assumed so short that the factors [math]\alpha, \beta, \gamma, \sigma[/math] can be considered approximately constant. Collecting the expressions of the factors influencing the temporal variation of plankton population, [math]dP/dt[/math] and the nutrient concentration, [math]dN/dt[/math], we arrive at the equations:

[math]dP/dt = B_P+G_P+S_P = (\beta \, N - \gamma - \sigma ) \, P , \qquad (A1)[/math]

[math]dN/dt = B_N +G_N + A + Q = \rho (\gamma - \beta \, N) \, P - \alpha \, N + Q . \qquad (A2)[/math]

These coupled nonlinear equations are known as the Lotka-Volterra equations. The solution is not straightforward. It depends not only on the coefficients appearing in the equations but also on the initial conditions [math]P=P_0, \, N=N_0[/math] at time [math]t=0[/math]. Different types of long-term behavior of [math]P[/math] and [math]N[/math] may result: convergence to a static equilibrium state, to a cyclic state or to a state of chaotic fluctuations around certain attractors. We will not consider the long-term behavior, especially because the assumption of constant coefficients does not hold. For a qualitative understanding of the short-term behavior we will first consider the equilibrium solution of the Eqs. (A1) and (A2) corresponding to [math]dP/dt=0[/math] and [math]dN/dt[/math]. The equilibrium solution is given by the curves

[math]N_{eq}=\large\frac{\gamma + \sigma}{\beta}\normalsize, \quad P_{eq}=\large\frac{Q -\alpha N}{\rho (\beta N - \gamma)}\normalsize. \qquad (A3)[/math]

These curves are drawn in Fig. A1. The curves cut the [math]P,N[/math] phase plane in 4 phase sectors:

  • Green phase: [math]P[/math] and [math]N[/math] both increase;
  • Yellow phase: [math]P[/math] increases, [math]N[/math] decreases;
  • Red phase: [math]P[/math] and [math]N[/math] both decrease;
  • Blue phase: [math]P[/math] decreases and [math]N[/math] increases.


Fig. A1. Solution of the Lotka-Volterra equations (A1, A2), showing the coupled evolution of [math]P(t)[/math] and[math]N(t)[/math] in the [math]N-P[/math] phase plane (solid line). The model parameters are set as follows: [math]\alpha=0, \, \beta=0.1/N_0/day, \, \rho=10^{-3}, \, \gamma=0, \, \sigma=0.1/day, \, Q=7.5 \, 10^{-3} N_0/day, [/math] [math]N_0=0.1 g/m^3, P_0=6.10^{-5} g/m^3[/math]. From Huppert et al. (2002)[51].
Fig. A2. Temporal evolution of the plankton population [math]P(t)[/math] (solid line) and the nutrient concentration [math]N(t)[/math] (dashed line) according to the equations (A1, A2) with the same parameters as for Fig. A1.


Now consider the evolution graph in Fig. A1, which is obtained by numerical integration of the Eqs. (A1) and (A2) [51]. The bloom starts in the green phase sector, where initially the plankton biomass [math] P [/math] is very small. The environmental conditions (temperature, light) have just become favorable for nutrient uptake, i.e. the uptake efficiency factor [math]\beta [/math] has increased significantly. Shortly before (not shown in the figure), [math]\beta [/math] was much smaller and [math]N_ {eq} [/math] much larger, so that the plankton population was still in the blue phase (i.e. decreasing in size). Due to the increased uptake efficiency, the biomass [math]P[/math] is now increasing, but the increase is slow because the rate of increase is proportional to [math]P[/math] (cf. A1). Nutrient uptake is still quite low and the nutrient concentration is still increasing due to the supply [math]Q[/math]. However, with increasing population size [math]P[/math] the nutrient uptake also increases and at some moment the nutrient concentration [math]N[/math] changes from increasing to decreasing. The [math]P-N[/math] system enters the yellow phase, where [math]P[/math] will grow rapidly, because the rate of change [math](\beta \, N - \gamma - \sigma ) \, P [/math] (Eq. A1) is large and initially still increasing in spite of the decrease of [math]N[/math] (Eq. A2). The plankton bloom essentially takes place at the end of the green phase and the beginning of the yellow phase. The nutrient concentration decreases in the yellow phase and finally becomes so small that further growth of the plankton biomass is no longer possible. At that time the plankton biomass has reached its maximum and the [math]P-N[/math] system enters the red phase, where the plankton biomass decreases. The nutrient concentration continues its rapid decrease (cf. A2) because the uptake of nutrients by the plankton population is still high. However, the plankton population also declines at a fast rate because [math](\beta \, N - \gamma - \sigma) \, P [/math] (cf. A1) is large and negative. The collapse of the plankton population mainly takes place in the red sector. The decrease of the nutrient concentration ends when the uptake of nutrients has become very small, because of the small size of the plankton population. The [math]P-N[/math] system then enters the blue phase where [math]P[/math] decreases further, while [math]N[/math] increases, but both at a slow rate. The plankton bloom as a function of time, corresponding to the trajectory in the [math]P-N[/math] phase plane, is shown in Fig. A2. It illustrates the dramatic increase and collapse of the plankton biomass (5 orders of magnitude) in a short time. The peak of the bloom coincides with the strongest decrease of the nutrient concentration. The same coincidence is visible in the observation record of the plankton bloom and nutrient concentration in Fig. 2. It also appears, both in the model and the data, that the variation of the nutrient concentration is much less pronounced than the variation in the plankton biomass.

The nonlinearity of the plankton bloom equations has implications that may appear surprising at first sight. One may notice, for instance, that complete nutrient depletion is not a necessary condition for the crash of the plankton population. The point is that below a certain level, which is not necessarily very small, the nutrient concentration is insufficient to sustain a very large plankton population.

The maximum plankton population size that can be reached during the bloom is not linearly related to the initial nutrient concentration [math]N_0[/math] and the initial population size [math]P_0[/math]. Depending on the values of [math]N_0, P_0[/math], it can happen that an increase of either [math]N_0[/math] or [math]P_0[/math] leads to a smaller bloom size, instead of a larger size[51]. According to the model, the nutrient concentration that is reached when the population enters the yellow phase determines to a large degree the maximum population size that can be reached during the yellow phase. This feature is not visible in the observations presented in Fig. 2. In fact, other factors such as sunlight and temperature also play a role. In the case of Fig. 2, it appears that the largest blooms are correlated with low salinity values. This points to a change in the type of water masses present at the measuring site. Stratification effects (suppression of vertical mixing) may also play a role in triggering plankton blooms (Berdalet et al., 2014[14]).

When all the factors influencing the plankton bloom dynamics remain constant in time, the bloom model predicts that the system will tend to the equilibrium state [math]N_{eq}, P_{eq}[/math] (eq. A3), which is stable if the nutrient loss rate [math]\alpha[/math] is sufficiently small. In practice, however, the temporal variation of the factors influencing the bloom dynamics prevents the establishment of an equilibrium state. When time dependency of the factors in the model equations (A1, A2) is taken into account, the multi-annual behavior of model simulations can exhibit unpredictable chaotic fluctuations (Huppert et al., 2005[52]). In case of a seasonally fluctuating uptake efficiency [math]\beta(t)[/math], the model produces chaotic behavior if the nutrient influx [math]Q[/math] is low and the mortality rate [math]\sigma[/math] is large.


Related articles

Marine Plankton
Nutrient conversion in the marine environment
Which resource limits coastal phytoplankton growth/ abundance: underwater light or nutrients?
Possible consequences of eutrophication
Eutrophication in coastal environments
Open ocean habitat
Shelf sea exchange with the ocean
Harmful algal bloom
Estuarine turbidity maximum
Plankton remote sensing
Plankton remote sensing North Sea
Differentiation of major algal groups by optical absorption signatures
Remote sensing of zooplankton
Light fields and optics in coastal waters
The Continuous Plankton Recorder (CPR)
Diversity and classification of marine benthic algae
Functional metabolites in phytoplankton


External sources

Phytoplankton
Algae
Marine primary production


References

  1. ↑ Sournia, A., Chretiennot-Dinet, M.-J. and Ricard M. 1991. Marine phytoplankton: how many species in the world ocean? Journal of Plankton Research 13: 1093–1099
  2. ↑ Andersen, R.A. 1992. Diversity of eukaryotic algae. Biodiversity and Conservation 1: 267-292
  3. ↑ Edwards, K. F., Klausmeier, C. A. and Litchman, E. 2011. Evidence for a three-way trade-off between nitrogen and phosphorus competitive abilities and cell size in phytoplankton. Ecology 92: 2085–2095
  4. ↑ Burson, A., Stomp, M., Greenwell, E., Grosse, J. and Huisman, J. 2018. Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community. Ecology 99: 1108–1118
  5. ↑ Redfield, A.C. 1958. The Biological Control of Chemical Factors in the Environment. American Scientist 46: 205-221
  6. ↑ Field, C. B., Behrenfeld, M. J., Randerson, J. T. and Falkowski, P. 1998. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281: 237–240
  7. ↑ Flynn, K.J., Stoecker, D.K., Mitra, A., Raven, J.A., Glibert, P.M., Hansen, P.J., Graneli, E. and Burkholder, J.M. 2013. Misuse of the phytoplankton-zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types. J. Plankton Res. 35: 3–11
  8. ↑ Blondeau-Patissier, D., Gower, J.F.R., Dekker, A.G., Phinn, S.R. and Brando, V.E. 2014. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography 123: 123–144
  9. ↑ Davidson, K., Gowen, R.J., Tett, P., Bresnan, E., Harrison, P.J., McKinney, A., Milligan, S., Mills, D.K., Silke, J. and Crooks, A.M. 2012. Harmful plankton blooms: How strong is the evidence that nutrient ratios and forms influence their occurrence? Estuar. Coast. Shelf Sci. 115: 399–413
  10. ↑ Bissinger, J. E., Montagnes, D. J. S., Sharples, J., and Atkinson, D. 2008. Predicting marine phytoplankton maximum growth rates from temperature: improving on the Eppley curve using quantile regression. Limnol. Oceanogr. 53: 487–493
  11. ↑ Boyd, P.W., Rynearson, T.A,. Armstrong, E.A., Fu, F., Hayashi, K., et al. 2013. Marine Phytoplankton Temperature versus Growth Responses from Polar to Tropical Waters – Outcome of a Scientific Community-Wide Study. PLoS ONE 8(5): e63091
  12. ↑ Wagner-Cremer, F., Steur, F. van Wezel, R., Verweij, G. and Kouwets, F. 2018. Wat momentopnamen van fytoplankton ons kunnen vertellen over de geschiedenis van de kust. H2O-Online / 19 januari 2018
  13. ↑ Taylor, J. R. and Ferrari, R. 2011. Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms. Limnology and Oceanography 56: 2293–2307
  14. ↑ 14.0 14.1 Berdalet, E., McManus, M.A., Ross, O.N., Burchard, H., Chavez, F.P., Jaffe, J.S., Jenkinson, I.R., Kudela, R., Lips, I., Lips, U., Lucas, A., Rivas, D., Ruiz-delaTorre, M.C., Ryan, J., Sullivan, J.M. and Yamazaki, H. 2014. Understanding harmful algae in stratified systems: Review of progress and future directions. Deep-Sea Research II 101: 4–20
  15. ↑ Sigman, D. M. and Hain, M. P. 2012. The Biological Productivity of the Ocean. Nature Education Knowledge 3: 21
  16. ↑ 16.0 16.1 Glibert, P.M. 2020. Harmful algae at the complex nexus of eutrophication and climate change. Harmful Algae 91: 101583
  17. ↑ Van de Poll, W.H., Kulk, G., Timmermans, K.R., Brussaard, C.P.D., van der Woerd, H.J., Kehoe, M.J., Mojica, K.D.A., Visser, R.J.W., Rozema, P.D. and Buma, A.G.J. 2013. Phytoplankton chlorophyll a biomass, composition, and productivity along a temperature and stratification gradient in the Northeast Atlantic Ocean. Biogeosciences 10: 4227–4240
  18. ↑ Hutchinson, G.E. 1961. The paradox of the plankton. American Naturalist 95: 137–147
  19. ↑ 19.0 19.1 Glibert, P.M. 2017. Eutrophication, harmful algae and biodiversity - challenging paradigms in a world of complex nutrient changes. Mar. Poll. Bull. 124: 591–606
  20. ↑ 20.0 20.1 Philippart, C.J.M., Beukema, J.J., Cadée, G.C., Dekker, R., Goedhart, P.W., van Iperen, J.M., Leopold, M.F. and Herman, P.M.J. 2007. Impacts of nutrient reduction on coastal communities. Ecosystems 10: 95-118
  21. ↑ Bharathia, M.D., Sarmaa, V.V.S.S. and Ramaneswaric, K. 2018. Intra-annual variations in phytoplankton biomass and its composition in the tropical estuary: Influence of river discharge. Marine Pollution Bulletin 129: 14–25
  22. ↑ Anderson, N.J. 2000. Diatoms, temperature and climate change. Eur. J. Phycol. 35: 307–314
  23. ↑ 23.0 23.1 Glibert, P.M. 2016. Margalef revisited: A new phytoplankton mandala incorporating twelve dimensions, including nutritional physiology. Harmful Algae 55: 25–30
  24. ↑ Margalef, R. 1978. Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanologica Acta 1: 493-508
  25. ↑ Klausmeier, C.A. and Litchman, E. 2001. Plankton games: The vertical distribution of phytoplankton in poorly mixed water columns. Limnol. Oceanogr. 46: 1998–2007
  26. ↑ Los, F.J. and Wijsman, J.M.W. 2007. Application of a validated primary production model (BLOOM) as a screening tool for marine, coastal and transitional waters. Journal of Marine Systems 64: 201-215
  27. ↑ Pahlow, M. 2005. Linking chlorophyll-nutrient dynamics to the Redfield N:C ratio with a model of optimal phytoplankton growth. Mar. Ecol. Prog. Ser. 287: 33−43
  28. ↑ Harvey, E.L. and Menden-Deuer, S. 2012. Predator-Induced Fleeing Behaviors in Phytoplankton: A New Mechanism for Harmful Plankton Bloom Formation? PLoS ONE 7(9): e46438
  29. ↑ Branco, P., Egas, M., Hall, S.R. and Huisman, J. 2020. Why Do Phytoplankton Evolve Large Size in Response to Grazing? The American Naturalist 195(1)
  30. ↑ Baudoux, A.-C. 2007. The role of viruses in marine phytoplankton mortality. Thesis Groningen University, 148 pp
  31. ↑ Mojica, K.D.A., Huisman, J., Wilhelm, S.W. and Brussaard, C.P.D. 2016. Latitudinal variation in virus-induced mortality of phytoplankton across the North Atlantic Ocean. The ISME Journal 10: 500–513
  32. ↑ Llort, J., Langlais, C., Matear, R., Moreau, S., Lenton, A. and Strutton, P.G. 2018. Evaluating Southern Ocean Carbon Eddy-Pump From Biogeochemical-Argo Floats. Journal of Geophysical Research Oceans 123: 971–984
  33. ↑ DeVries, T., Le Quéré, C., Andrew, R.O., Berthet, S., Hauck, J., Ilyina, T., Landschützer, P., Lenton, A., Lima, I.D., Nowicki, M., Schwinger, J. and Séférian, R. 2019. Decadal trends in the ocean carbon sink. PNAS 116: 11646–11651
  34. ↑ Nixon, S. W. 1988. Physical energy inputs and the comparative ecology of lake and marine ecosystems. Limnol. Oceanogr. 33: 1005–1025
  35. ↑ Nixon, S.W. and Buckley, B.A. 2002. “A strikingly rich zone” – Nutrient enrichment and secondary production in coastal marine ecosystems. Estuaries 25: 782–796
  36. ↑ Smayda, T.J. 1997. Harmful plankton blooms: their ecophysiology and general relevance to phytoplankton blooms in the sea. Limnology and Oceanography 42: 1137–1153
  37. ↑ Zingone, A. and Wyatt, T. 2004. Harmful plankton blooms: keys to the understanding of the phytoplankton ecology. In: Robinson, A.R., McCarthy, J. and Rothschild, B.J. (eds.) The Sea. Harvard University Press, Harvard, pp. 867-926
  38. ↑ Smayda T.J. 1997. Harmful plankton blooms: their ecophysiology and general relevance to phytoplankton blooms in the sea. Limnology and Oceanography 42: 1137–1153
  39. ↑ Hallegraeff, G.M. 1993. A review of harmful plankton blooms and their apparent global increase. Phycologia 32: 79–99
  40. ↑ Anderson, D. 2012. HABs in a changing world: a perspective on harmful plankton blooms, their impacts, and research and management in a dynamic era of climatic and environmental change. Harmful Algae 2012, 2014
  41. ↑ Berdalet, E., Fleming, L.E., Gowen, R., Davidson, K., Hess, P., Backer, L.C., Moore, S.K., Hoagland, P. and Enevoldsen, H. 2016. Marine harmful plankton blooms, human health and wellbeing: challenges and opportunities in the 21st century. J. Mar. Biol. Assoc. U.K. 96: 61–91
  42. ↑ Wells, M.L., Karlson, B., Wulff, A., Kudela, R., Trick, C., Asnaghi, V., Berdalet, E., Cochlan, W., Davidson, K., De Rijcke, M., Dutkiewicz, S., Hallegraeff, G., Flynn, K.J., Legrand, C., Paerl, H., Silke, J., Suikkanen, S., Thompson, P. and Trainer, V.L. 2020. Future HAB science: Directions and challenges in a changing climate. Harmful Algae 91: 101632
  43. ↑ Westberry, T.K., Silsbe, G.M. and Behrenfeld, M.J. 2023. Gross and net primary production in the global ocean: An ocean color remote sensing perspective. Earth-Science Reviews 237: 104322
  44. ↑ Howarth, R.W. 1988. Nutrient limitation of net primary production in marine ecosystems. Annual Rev. Ecol. Syst. 19: 89–110
  45. ↑ Cloern, J.E., Foster, S.Q. and Kleckner, A.E. 2014. Phytoplankton primary production in the world's estuarine-coastal ecosystems. Biogeosciences 11: 2477–2501
  46. ↑ Liu, B., de Swart, H.E. and de Jonge, V.N. 2018. Phytoplankton bloom dynamics in turbid, well-mixed estuaries: a model study. Estuarine, Coastal and Shelf Science 211: 137-151
  47. ↑ McSweeney, J.M., Chant, R.J., Wilkin, J.L. and Sommerfield, C.K. 2016. Suspended sediment impacts on light-limited productivity in the Delaware estuary. Estuaries and Coasts 40: 977–993
  48. ↑ De Jonge, V.N. and van Beusekom, J.E.E. 1992. Contribution of resuspended microphytobenthos to total phytoplankton in the Ems estuary and its possible role for grazers. Netherlands Journal of Sea Research 30: 91–105
  49. ↑ Caffrey, J.M. 2004. Factors controlling net ecosystem metabolism in US estuaries. Estuaries 27: 90–101
  50. ↑ Gattuso, J.-P., Frankignoulle, M. and Wollast, R. 1998. Carbon and carbonate metabolism in coastal aquatic ecosystems. Annual Rev. Ecol. Syst. 29: 405–433
  51. ↑ 51.0 51.1 51.2 51.3 Huppert, A., Blasius, B. and Stone, L. 2002. A Model of Phytoplankton Blooms. The American Naturalist 159: 156-171
  52. ↑ Huppert, A., Blasius, B., Olinky, R. and Stone, L. 2005. A Model for Seasonal Phytoplankton Blooms. Journal of Theoretical Biology 236: 276–290


The main author of this article is Job Dronkers
Please note that others may also have edited the contents of this article.
Citation: Job Dronkers (2026): Plankton bloom. Available from http://www.coastalwiki.org/wiki/Plankton_bloom [accessed on 27-05-2026]
Retrieved from "https://www.coastalwiki.org/w/index.php?title=Plankton_bloom&oldid=82046"