Geosci. Model Dev., 9, 4071–4085, 2016 www.geosci-model-dev.net/9/4071/2016/ doi:10.5194/gmd-9-4071-2016 © Author(s) 2016. CC Attribution 3.0 License. PhytoSFDM version 1.0.0: Phytoplankton Size and Functional Diversity Model Esteban Acevedo-Trejos 1 , Gunnar Brandt 1,a , S. Lan Smith 2 , and Agostino Merico 1,3 1 Systems Ecology Group, Leibniz Center for Tropical Marine Ecology, Fahrenheitstrasse 6, 28359 Bremen, Germany 2 Marine Ecosystem Dynamics Research Group, Research and Development Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan 3 Faculty of Physics & Earth Sciences, Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany a current address: Brockmann Consult, Max-Planck-Str. 7, 21052 Geesthacht, Germany Correspondence to: Esteban Acevedo-Trejos (esteban.acevedo@leibniz-zmt.de) Received: 28 April 2016 – Published in Geosci. Model Dev. Discuss.: 30 May 2016 Revised: 30 September 2016 – Accepted: 21 October 2016 – Published: 14 November 2016 Abstract. Biodiversity is one of the key mechanisms that fa- cilitate the adaptive response of planktonic communities to a fluctuating environment. How to allow for such a flexible re- sponse 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. Alter- natively, models of aggregate community properties focus on macroecological quantities such as total biomass, mean trait, and trait variance (or functional trait diversity), thus reduc- ing the observed natural complexity to a few mathematical expressions. We developed the PhytoSFDM modelling tool, which can resolve species discretely and can capture aggre- gate 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 zero- dimensional physical scheme and can be applied to any loca- tion of the global ocean. We show that aggregate community models reduce computational complexity while preserving relevant macroecological features of phytoplankton commu- nities. Compared to species-explicit models, aggregate mod- els 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 im- plementations into spatially resolved physical settings (one- dimensional and three-dimensional). With PhytoSFDM we embrace the idea of promoting open-source software and en- courage scientists to build on this modelling tool to further improve our understanding of the role that biodiversity plays in shaping marine ecosystems. 1 Introduction Numerical models are simplified abstractions of complex phenomena. They are engineered for the problem at hand and cannot be designed to maximize simultaneously the three key requirements of generality, precision, and realism, be- cause one of these must be sacrificed in favour of the other two (Levins, 1966). Marine ecosystem models are no excep- tions, and the scientific community has questioned the trend towards increasing model complexity in terms of large num- bers of state variables and parameters (Fulton et al., 2003; Anderson, 2005; Hood et al., 2006; Anderson, 2010). Alter- natives such as trait-based models have been put forward as a way to simplify overly parameterized ecosystem models (Follows and Dutkiewicz, 2011). In the past 2 decades, trait-based models of planktonic ecosystems have become important tools for elucidating the fundamental mechanisms behind emergent patterns of com- munity structure and diversity. 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; Bar- ton et al., 2010; Banas, 2011; Ward et al., 2012; Smith et al., 2015). Alternatively, models have been developed that Published by Copernicus Publications on behalf of the European Geosciences Union.