PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model

Abstract

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.

CITRATE 1.0: Phytoplankton continuous trait-distribution model with one-dimensional physical transport applied to the North Pacific

“…where λ is a positive constant. Whether the power law or the lognormal distribution fits better to empirical data has been widely debated in the literature, and many results show that both can fit the data equally well (Allen et al, 2001;Mitzenmacher, 2004;Clauset et al, 2009). This is not surprising given that the two distributions are intrinsically connected (Mitzenmacher, 2004;Newman, 2005).…”

CITRATE 1.0: Phytoplankton continuous trait-distribution model with one-dimensional physical transport applied to the Northwest Pacific

“…The optimized u value was much higher than in Acevedo-Trejos et al (2016). Reducing u to 0.05 led to worse fits to the size-fractionated chlorophyll since lower size variance failed to capture the observed size scatter.…”

“…Besides the improved computational efficiency (Acevedo-Trejos et al, 2016), the most important advantage of the continuous trait-based 'adaptive dynamics' approach is expressed well in the following quote from Bak (1996): "If, following traditional scientific methods, we concentrate on an accurate description of the details, we lose perspective." (p. 10) and, "It is a futile endeavour to try to explain most natural phenomena in detail by starting from particle physics and following the trajectories of all particles."…”

“…Continuous trait-based models have been developed to address the above questions (Wirtz and Eckhardt, 1996;Norberg et al, 2001;Bruggeman, 2009;Merico et al, 2009Merico et al, , 2014Terseleer et al, 2014;Acevedo-Trejos et al, 2016;Smith et al, 2016). Instead of modeling the dynamics of individual species, continuous trait-based models or so-called "adaptive dynamics" models focus on macroscopic or aggregate properties of a community such as total biomass, average trait, and trait variance by assuming that phytoplankton traits follow some distribution (usually Gaussian) (Smith et al, 2011).…”

Measures and Approaches in Trait-Based Phytoplankton Community Ecology – From Freshwater to Marine Ecosystems

“…In phytoplankton body size is usually estimated as cell size along the maximum linear dimension (abbreviated as MLD or GALD: greatest axial linear dimension). The use of cell size is common to both the explicit trait and morphospecies frameworks and lends itself well to ecosystem modeling (e.g., Acevedo-Trejos et al, 2016).…”