Sixty Years of Sverdrup: A Retrospective of Progress in the Study of Phytoplankton Blooms

Fischer, Alexis D.; Moberg, Emily; Alexander, Harriet; Brownlee, Emily F.; Hunter-Cevera, Kristen R.; Pitz, Kathleen J.; Rosengard, Sarah Z.; Sosik, Heidi M.

doi:10.5670/oceanog.2014.26

Cited by 50 publications

(35 citation statements)

References 90 publications

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“…In this context, the assumption is that the phytoplankton carbon biomass concentration ([ C phyto ]) is well coupled to the depth‐integrated inventory (∑C phyto ), and bloom initiation is typically estimated as the point in time when surface concentrations begin to increase substantially [ Siegel et al ., ]. However, some observations indicate that depth‐integrated biomass can increase before the mixed layer shoals above this critical depth [ Townsend et al ., ; Eilertsen , ; Dale et al ., ] implying that bloom inception may be influenced by other mechanisms including dilution (or disturbance) recovery [ Fischer et al ., ].…”

Section: Introductionmentioning

confidence: 99%

“…For a bloom to occur, the springtime mixed layer depth must shoal above some critical level for depth-averaged PAR to drive levels of community photosynthesis above losses [Gran and Braarud, 1935;Riley, 1946;Sverdrup, 1953]. In this context, the assumption is that the phytoplankton carbon biomass concentration ([C phyto ]) is well coupled to the depth-integrated inventory ( P C phyto ), and bloom initiation is typically estimated as the point in time when surface concentrations begin to increase substantially [Siegel et al, 2002] both strong or poor cell division rates depending on iron/light availability and trophic decoupling • Implicit evidence in the remote sensing data supports these unique mechanistic pathways as well as a strong but variable grazing control some observations indicate that depth-integrated biomass can increase before the mixed layer shoals above this critical depth [Townsend et al, 1992;Eilertsen, 1993;Dale et al, 1999] implying that bloom inception may be influenced by other mechanisms including dilution (or disturbance) recovery [Fischer et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

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Variability in the mechanisms controlling Southern Ocean phytoplankton bloom phenology in an ocean model and satellite observations

Rohr

Long

Kavanaugh

et al. 2017

Global Biogeochemical Cycles

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A coupled global numerical simulation (conducted with the Community Earth System Model) is used in conjunction with satellite remote sensing observations to examine the role of top-down (grazing pressure) and bottom-up (light, nutrients) controls on marine phytoplankton bloom dynamics in the Southern Ocean. Phytoplankton seasonal phenology is evaluated in the context of the recently proposed "disturbance-recovery" hypothesis relative to more traditional, exclusively "bottom-up" frameworks. All blooms occur when phytoplankton division rates exceed loss rates to permit sustained net population growth; however, the nature of this decoupling period varies regionally in Community Earth System Model. Regional case studies illustrate how unique pathways allow blooms to emerge despite very poor division rates or very strong grazing rates. In the Subantarctic, southeast Pacific small spring blooms initiate early cooccurring with deep mixing and low division rates, consistent with the disturbance-recovery hypothesis. Similar systematics are present in the Subantarctic, southwest Atlantic during the spring but are eclipsed by a subsequent, larger summer bloom that is coincident with shallow mixing and the annual maximum in division rates, consistent with a bottom-up, light limited framework. In the model simulation, increased iron stress prevents a similar summer bloom in the southeast Pacific. In the simulated Antarctic zone (70°S-65°S) seasonal sea ice acts as a dominant phytoplankton-zooplankton decoupling agent, triggering a delayed but substantial bloom as ice recedes. Satellite ocean color remote sensing and ocean physical reanalysis products do not precisely match model-predicted phenology, but observed patterns do indicate regional variability in mechanism across the Atlantic and Pacific.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variability in the mechanisms controlling Southern Ocean phytoplankton bloom phenology in an ocean model and satellite observations

Rohr

Long

Kavanaugh

et al. 2017

Global Biogeochemical Cycles

View full text Add to dashboard Cite

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“…This hypothesis suggests that spring blooms occur if the mixed layer depth shoals from winter to spring to become shallower than

H_{C}

. By linking phytoplankton growth to the shoaling of the mixed layer beyond the critical depth, the hypothesis provides a simple explanation for spring blooms and is widely recognized as the primary principle in the literature (Fischer et al ). However, there have been observations of phytoplankton blooms before the shoaling of the mixed layer depth, leading to the proposal of several additional mechanisms.…”

Section: Introductionmentioning

confidence: 99%

A Lagrangian View of Spring Phytoplankton Blooms

Kida

Ito

2017

JGR Oceans

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The mechanisms of spring phytoplankton blooms are investigated from a Lagrangian framework by using a Lagrangian NPZD model that can track the movement and transfers of nutrient parcels in a turbulent environment. The model reveals that the onset of spring blooms depends on the cumulative euphotic age, which is the total time that inorganic nutrient is exposed to light before the photosynthetic conversion to phytoplankton biomass. A spring bloom, defined as a tenfold increase of near‐surface phytoplankton, occurs when this cumulative euphotic age is approximately μeff−1·italicln⁡10, where μeff is the effective growth rate in the euphotic layer, regardless of the underlying mechanism. If the turbulent layer depth is shallower than the critical depth and turbulence is strong, nutrient parcels accumulate enough light exposure through multiple entries to the sun‐lit zone near the surface. If turbulence is weak, as that considered in the critical turbulence theory, the accumulation of the light exposure depends on the residence time of the nutrients parcels near the surface. The spectral shape of the cumulative euphotic age can clearly distinguish these two modes of spring blooms. The spectrum shows a peak at the theoretical growth timescale when multiple entries become important, while it shows a maximum near age zero that decays with age when the near‐surface residence time becomes important. Mortality increases the cumulative euphotic age necessary for a bloom but does not affect the spectral shape, suggesting that it does not alter the primary mechanism behind the accumulation of cumulative euphotic age.

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“…However, this hypothesis has been more recently brought into question as bloom formation has been observed to start earlier than expected (Mahadevan et al, 2012) and in the absence of stratification (Townsend et al, 1992;Eilertsen, 1993). Several new theories have now been developed to explain these occurrences (reviewed in Behrenfeld and Boss, 2014;Fischer et al, 2014;Lindemann and St. John, 2014).…”

Section: Introductionmentioning

confidence: 99%

Phytoplankton dynamics in contrasting early stage North Atlantic spring blooms: composition, succession, and potential drivers

et al. 2015

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Abstract. The spring bloom is a key annual event in the phenology of pelagic ecosystems, making a major contribution to the oceanic biological carbon pump through the production and export of organic carbon. However, there is little consensus as to the main drivers of spring bloom formation, exacerbated by a lack of in situ observations of the phytoplankton community composition and its evolution during this critical period.We investigated the dynamics of the phytoplankton community structure at two contrasting sites in the Iceland and Norwegian basins during the early stage (25 March-25 April) of the 2012 North Atlantic spring bloom. The plankton composition and characteristics of the initial stages of the bloom were markedly different between the two basins. The Iceland Basin (ICB) appeared well mixed down to > 400 m, yet surface chlorophyll a (0.27-2.2 mg m −3 ) and primary production (0.06-0.66 mmol C m −3 d −1 ) were elevated in the upper 100 m. Although the Norwegian Basin (NWB) had a persistently shallower mixed layer (< 100 m), chlorophyll a (0.58-0.93 mg m −3 ) and primary production (0.08-0.15 mmol C m −3 d −1 ) remained lower than in the ICB, with picoplankton (< 2 µm) dominating chlorophyll a biomass. The ICB phytoplankton composition appeared primarily driven by the physicochemical environment, with periodic events of increased mixing restricting further increases in biomass. In contrast, the NWB phytoplankton community was potentially limited by physicochemical and/or biological factors such as grazing.Diatoms dominated the ICB, with the genus Chaetoceros (1-166 cells mL −1 ) being succeeded by Pseudo-nitzschia (0.2-210 cells mL −1 ). However, large diatoms (> 10 µm) were virtually absent (< 0.5 cells mL −1 ) from the NWB, with only small nano-sized (< 5 µm) diatoms (i.e. Minidiscus spp.) present (101-600 cells mL −1 ). We suggest microzooplankton grazing, potentially coupled with the lack of a seed population of bloom-forming diatoms, was restricting diatom growth in the NWB, and that large diatoms may be absent in NWB spring blooms. Despite both phytoplankton communities being in the early stages of bloom formation, different physicochemical and biological factors controlled bloom formation at the two sites. If these differences in phytoplankton composition persist, the subsequent spring blooms are likely to be significantly different in terms of biogeochemistry and trophic interactions throughout the growth season, with important implications for carbon cycling and organic matter export.

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Sixty Years of Sverdrup: A Retrospective of Progress in the Study of Phytoplankton Blooms

Cited by 50 publications

References 90 publications

Variability in the mechanisms controlling Southern Ocean phytoplankton bloom phenology in an ocean model and satellite observations

Variability in the mechanisms controlling Southern Ocean phytoplankton bloom phenology in an ocean model and satellite observations

A Lagrangian View of Spring Phytoplankton Blooms

Phytoplankton dynamics in contrasting early stage North Atlantic spring blooms: composition, succession, and potential drivers

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