Flexible phytoplankton functional type (FlexPFT) model: size-scaling of traits and optimal growth

Smith, S. Lan; Pahlow, Markus; Merico, Agostino; Acevedo‐Trejos, Esteban; Sasai, Yoshikazu; Yoshikawa, Chisato; Sasaoka, Kosei; Fujiki, Tetsuichi; Matsumoto, Kazuhiko; Honda, Makio C.

doi:10.1093/plankt/fbv038

Cited by 42 publications

(72 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Eppley et al, 1969;Falkowski, 1975;Burmaster, 1979;Droop, 1983). This discussion regained attention during recent years and the sole application of the Monod equation is currently viewed as a considerable drawback when simulating plankton growth under transient (unbalanced growth) conditions (Flynn, 2003;Smith et al, 2009Smith et al, , 2014Smith et al, , 2015Franks, 2009). …”

Section: Algal Growth and Nutrient Limitationmentioning

confidence: 99%

“…by considering the adaptation of traits towards optimal trade-offs (e.g. Wirtz and Pahlow, 2010;Dutkiewicz et al, 2009;Smith et al, 2015), or by accounting for allometric relationships in growth and plankton interaction (e.g. Banas, 2011;Acevedo-Trejos et al, 2015), or by using microbial traits in a functional gene approach (Reed et al, 2014).…”

Section: Modelling Prospectsmentioning

confidence: 99%

See 1 more Smart Citation

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

show abstract

Section: Algal Growth and Nutrient Limitationmentioning

confidence: 99%

Section: Modelling Prospectsmentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Phytoplankton cells change their traits (e.g. nutrient channel, enzyme) in response to ambient nutrient concentrations, and typically large (small) cells adapt to low (high) light and high (low) nutrient concentrations (Smith et al, 2015). In the NSI-MEM, the effect of nutrient-uptake responses by plankton acclimated to different ambient nutrient conditions is applied as an OU kinetic formulation, but the effect of photo-acclimation has not yet been introduced.…”

Section: Physiological Parameter Changes With Ambient Conditionsmentioning

confidence: 99%

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

et al. 2018

View full text Add to dashboard Cite

Abstract. Ecosystem models are used to understand ecosystem dynamics and ocean biogeochemical cycles and require optimum physiological parameters to best represent biological behaviours. These physiological parameters are often tuned up empirically, while ecosystem models have evolved to increase the number of physiological parameters. We developed a three-dimensional (3-D) lower-trophic-level marine ecosystem model known as the Nitrogen, Silicon and Iron regulated Marine Ecosystem Model (NSI-MEM) and employed biological data assimilation using a micro-genetic algorithm to estimate 23 physiological parameters for two phytoplankton functional types in the western North Pacific. The estimation of the parameters was based on a onedimensional simulation that referenced satellite data for constraining the physiological parameters. The 3-D NSI-MEM optimized by the data assimilation improved the timing of a modelled plankton bloom in the subarctic and subtropical regions compared to the model without data assimilation. Furthermore, the model was able to improve not only surface concentrations of phytoplankton but also their subsurface maximum concentrations. Our results showed that surface data assimilation of physiological parameters from two contrasting observatory stations benefits the representation of vertical plankton distribution in the western North Pacific.

show abstract

“…Most of these models describe the phytoplankton community by a discrete representation of many species or functional groups (Baird and Suthers, 2007;Follows et al, 2007;Bruggeman and Kooijman, 2007;Barton et al, 2010;Banas, 2011;Ward et al, 2012;Smith et al, 2015). Alternatively, models have been developed that treat the whole phytoplankton species assemblage as a single entity (Wirtz and Eckhardt, 1996;Norberg et al, 2001;Merico et al, 2009;Bruggeman, 2009;Wirtz, 2013;Wirtz and Sommer, 2013;Terseleer et al, 2014;Acevedo-Trejos et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…discrete and aggregate) relies on the use of a key trait, for which relationships with other traits can be formulated. Cell size is recognized as one of the most important traits for characterizing phytoplankton communities (Litchman and Klausmeier, 2008;Litchman et al, 2010;Finkel et al, 2010;Marañón, 2015), and it has been commonly used in plankton ecosystem models (Baird and Suthers, 2007;Banas, 2011;Ward et al, 2012;Wirtz, 2013;Wirtz and Sommer, 2013;Terseleer et al, 2014;Acevedo-Trejos et al, 2015;Smith et al, 2015). This morphological trait affects trophic organization of foodwebs and the sequestration of CO 2 into the ocean interior (Chisholm, 1992).…”

Section: Introductionmentioning

confidence: 99%

PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. Biodiversity is one of the key mechanisms that facilitate the adaptive response of planktonic communities to a fluctuating environment. How to allow for such a flexible response in marine ecosystem models is, however, not entirely clear. One particular way is to resolve the natural complexity of phytoplankton communities by explicitly incorporating a large number of species or plankton functional types. Alternatively, models of aggregate community properties focus on macroecological quantities such as total biomass, mean trait, and trait variance (or functional trait diversity), thus reducing the observed natural complexity to a few mathematical expressions. We developed the PhytoSFDM modelling tool, which can resolve species discretely and can capture aggregate community properties. The tool also provides a set of methods for treating diversity under realistic oceanographic settings. This model is coded in Python and is distributed as open-source software. PhytoSFDM is implemented in a zerodimensional physical scheme and can be applied to any location of the global ocean. We show that aggregate community models reduce computational complexity while preserving relevant macroecological features of phytoplankton communities. Compared to species-explicit models, aggregate models are more manageable in terms of number of equations and have faster computational times. Further developments of this tool should address the caveats associated with the assumptions of aggregate community models and about implementations into spatially resolved physical settings (onedimensional and three-dimensional). With PhytoSFDM we embrace the idea of promoting open-source software and encourage scientists to build on this modelling tool to further improve our understanding of the role that biodiversity plays in shaping marine ecosystems.

show abstract

Flexible phytoplankton functional type (FlexPFT) model: size-scaling of traits and optimal growth

Cited by 42 publications

References 38 publications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model

Contact Info

Product

Resources

About