Planktonic biomass size spectra: an emergent property of size-dependent physiological rates, food web dynamics, and nutrient regimes

Taniguchi, Darcy A. A.; Franks, Peter J. S.; Poulin, Francis

doi:10.3354/meps10968

Cited by 36 publications

(54 citation statements)

References 91 publications

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“…Growth rate is determined by temperature‐regulated maximum growth rate µ i , light availability I , and nutrient limitation described as a Michaelis‐Menten function with size‐dependent half saturation constant ks (Table ). Maximum growth rate was computed as a function of the size‐dependent maximum growth rate µmax at 20°C (Table ) and temperature T (°C), using a Q 10 of 1.62 (Taniguchi et al ):

μ_{i} = {normalμ max}_{i} 1 . 62^{true(T - 20 true) / 10}

…”

Section: An Explanatory Modelmentioning

confidence: 99%

“…I modified the grazing equation from the original approach of Taniguchi et al () by prescribing a low phytoplankton biomass pm (= 0.1 μM N) at which grazing was set to zero. This allowed a minimum phytoplankton biomass in the simulations comparable to the minimum Chl a concentrations measured in San Francisco Bay of about 0.5 mg m −3 .…”

Section: An Explanatory Modelmentioning

confidence: 99%

“…Central to this model are four allometric equations that prescribe growth and grazing parameters as power functions of cell size S (ESD, μm). These are built from a comprehensive compilation of published experimental measurements (Taniguchi et al ):

μ max at 20 ° C ({normald}^{- 1}) = 1.36 S^{- 0.16}

ks (μ M N) = 0.33 S^{0.48}

g max at 20 ° C ({normald}^{- 1}) = 33.96 S^{- 0.66}

kz (μ M N) = 17.92 S^{- 0.64}

…”

Section: An Explanatory Modelmentioning

confidence: 99%

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Why large cells dominate estuarine phytoplankton

Cloern

2017

Limnology & Oceanography

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Surveys across the world oceans have shown that phytoplankton biomass and production are dominated by small cells (picoplankton) where nutrient concentrations are low, but large cells (microplankton) dominate when nutrient-rich deep water is mixed to the surface. I analyzed phytoplankton size structure in samples collected over 25 yr in San Francisco Bay, a nutrient-rich estuary. Biomass was dominated by large cells because their biomass selectively grew during blooms. Large-cell dominance appears to be a characteristic of ecosystems at the land-sea interface, and these places may therefore function as analogs to oceanic upwelling systems. Simulations with a size-structured NPZ model showed that runs of positive net growth rate persisted long enough for biomass of large, but not small, cells to accumulate. Model experiments showed that small cells would dominate in the absence of grazing, at lower nutrient concentrations, and at elevated (158C) temperatures. Underlying these results are two fundamental scaling laws: (1) large cells are grazed more slowly than small cells, and (2) grazing rate increases with temperature faster than growth rate. The model experiments suggest testable hypotheses about phytoplankton size structure at the land-sea interface: (1) anthropogenic nutrient enrichment increases cell size; (2) this response varies with temperature and only occurs at mid-high latitudes; (3) large-cell blooms can only develop when temperature is below a critical value, around 158C; (4) cell size diminishes along temperature gradients from high to low latitudes; and (5) large-cell blooms will diminish or disappear where planetary warming increases temperature beyond their critical threshold."the biomass spectrum has an important future in marine ecology" (Platt 1985).A grand challenge of limnology and oceanography is to understand the processes that shape phytoplankton communities. Individual phytoplankton species appear while others disappear, for reasons largely unexplained, to reshape communities over time scales of weeks or even days. The challenge is visualized in Fig. 1, showing images of phytoplankton sampled at nearby locations in San Francisco Bay on different dates. The image conveys information about community variability at two levels: cell morphology that defines communities as assemblages of species, and cell size that ranges over 9 orders of magnitude (Finkel et al. 2010).The mechanisms of phytoplankton community variability at the species level remain mysterious. Experiments (Beninc a et al. 2008) and models (Huisman and Weissing 1999) demonstrate how species interactions, such as resource competition, can generate chaotic fluctuations in plankton food webs, even in the absence of environmental variability. Predictability of chaotic systems decays quickly over time and, as a result, "long-term prediction of species abundances can be fundamentally impossible" (Beninc a et al. 2008).

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μ_{i} = {normalμ max}_{i} 1 . 62^{true(T - 20 true) / 10}

…”

Section: An Explanatory Modelmentioning

confidence: 99%

Section: An Explanatory Modelmentioning

confidence: 99%

μ max at 20 ° C ({normald}^{- 1}) = 1.36 S^{- 0.16}

ks (μ M N) = 0.33 S^{0.48}

g max at 20 ° C ({normald}^{- 1}) = 33.96 S^{- 0.66}

kz (μ M N) = 17.92 S^{- 0.64}

…”

Section: An Explanatory Modelmentioning

confidence: 99%

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Why large cells dominate estuarine phytoplankton

Cloern

2017

Limnology & Oceanography

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“…Moreover, observations suggest that mesoscale turbulence might also shape the distribution of zooplankton types [51]. Addressing this question requires a model with more complexity in the description of grazers [52,53] and will be the subject of a subsequent study.…”

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confidence: 99%

The dynamical landscape of marine phytoplankton diversity

Lévy

Jahn

Dutkiewicz

et al. 2015

J. R. Soc. Interface.

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Observations suggest that the landscape of marine phytoplankton assemblage might be strongly heterogeneous at the dynamical mesoscale and submesoscale (10-100 km, days to months), with potential consequences in terms of global diversity and carbon export. But these variations are not well documented as synoptic taxonomic data are difficult to acquire. Here, we examine how phytoplankton assemblage and diversity vary between mesoscale eddies and submesoscale fronts. We use a multi-phytoplankton numerical model embedded in a mesoscale flow representative of the North Atlantic. Our model results suggest that the mesoscale flow dynamically distorts the niches predefined by environmental contrasts at the basin scale and that the phytoplankton diversity landscape varies over temporal and spatial scales that are one order of magnitude smaller than those of the basin-scale environmental conditions. We find that any assemblage and any level of diversity can occur in eddies and fronts. However, on a statistical level, the results suggest a tendency for larger diversity and more fast-growing types at fronts, where nutrient supplies are larger and where populations of adjacent water masses are constantly brought into contact; and lower diversity in the core of eddies, where water masses are kept isolated long enough to enable competitive exclusion.

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“…The microzooplankton GGE was taken directly from Taniguchi et al . (). Uncertainty ranges on virus GGE were calculated with upper and lower bounds of Eq.…”

Section: Resultsmentioning

confidence: 97%

An empirical model of carbon flow through marine viruses and microzooplankton grazers

Talmy

Beckett

Taniguchi

et al. 2019

Environmental Microbiology

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Summary Viruses and microzooplankton grazers represent major sources of mortality for marine phytoplankton and bacteria, redirecting the flow of organic material throughout the world's oceans. Here, we investigate the use of nonlinear population models of interactions between phytoplankton, viruses and grazers as a means to quantitatively constrain the flow of carbon through marine microbial ecosystems. We augment population models with a synthesis of laboratory‐based estimates of prey, predator and viral life history traits that constrain transfer efficiencies. We then apply the model framework to estimate loss rates in the California Current Ecosystem (CCE). With our empirically parameterized model, we estimate that, of the total losses mediated by viruses and microzooplankton grazing at the focal CCE site, 22 ± 3%, 46 ± 27%, 3 ± 2% and 29 ± 20% were directed to grazers, sloppy feeding (as well as excretion and respiration), viruses and viral lysate respectively. We identify opportunities to leverage ecosystem models and conventional mortality assays to further constrain the quantitative rates of critical ecosystem processes.

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Planktonic biomass size spectra: an emergent property of size-dependent physiological rates, food web dynamics, and nutrient regimes

Cited by 36 publications

References 91 publications

Why large cells dominate estuarine phytoplankton

Why large cells dominate estuarine phytoplankton

The dynamical landscape of marine phytoplankton diversity

An empirical model of carbon flow through marine viruses and microzooplankton grazers

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