PhytoSFDM is a modelling framework to quantify phytoplankton community structure and functional diversity
PhytoSFDM is a modelling framework for studying the size structure and the functional diversity of marine phytoplankton communities. The provided software consist of three modules that allows the user to calculate aggregate community properties of phytoplankton in a 0 dimensional physical setting at any particular location of the world’s ocean.
The structure of all model variants are based on the familiar NPZD form as in the seminal work of Fasham (1990), where ordinary differential equations for nutrients, phytoplankton, zooplankton, and detritus trace the fluxes between these state variables. Here we extended this traditional modelling structure by characterising the phytoplankton community with a trait (i.e. cell size) and a trade-off emerging from three allometric relationships between, cell size and: 1) phytoplankton nutrient uptake, 2) zooplankton grazing, and 3) phytoplankton sinking. In these models the size structure and size diversity of the phytoplankton community is modelled by explicitly quantifying a finite number of phytoplankton morphotypes, each type with a specific size value (i.e. full model); or by approximating the size distribution using a moment closure technique, where we only quantified the total biomass, the mean size, and the size variance of the community (i.e. aggregate models). These approaches are inspired by early works of Wirtz & Eckhardt (1996), Norberg et al. (2001), Bruggeman & Koojiman (2007), Bruggeman (2009) and Merico et al. (2009). Some examples of more recent applications of the moment-based approximation are Wirtz (2013), Wirtz & Sommer (2013), Terseleer et al. (2014), and Acevedo-Trejos et al. (2015).
The three main modules of the package are: example, sizemodels, and envforcing. The example module is the entry point of the package, which computes and compares the two main model structures (full and aggregate) and the four variance treatments (Unsustained, Fixed, Immigration and Trait Diffusion) at a testing location in the north Atlantic Ocean. The module sizemodels contains a single class with methods to quantify the phytoplankton size structure and their functional diversity. Also within this class we provide methods to: a) modify the default parameters, b) symbolically solve the derivatives of the fitness function with respect to the trait, and c) log-transform the mean trait and the trait variance. The last module envforcing consist of one class with methods to extract a spatially averaged forcing data provided in the NetCDF files. The climatological data is at a monthly resolution, thus, a method to interpolate to daily time step is also included in this module.
How to Install
We assume the user have a running version of Python 2.7.x and have permissions to write in the folder where the python distribution is installed. Still the package have not been tested in Python 3.x, but further developments of the package will be compatible to newer versions of Python. To install it the user would require the latest versions of pip and setuptools. Additional dependencies are: matplotlib (version 1.4.3 or greater), numpy (version 1.9.2 or greater), scipy (version 0.15.1 or greater), sympy (version 0.7.6.1 or greater) and basemap (verision 1.0.7 or greater).
To install the package using pip just type in a terminal (Unix like systems) or in a command prompt window (Windows systems):
$ pip install PhytoSFDM
To install the package from the tarball, just download the file from GitHub. Then untar and unzip the file with a specific software like WinRAR (in Windows) or type in a terminal (Unix like systems):
$ tar xvfz PhytoSFDM-X.X.X.tar.gz
where the Xs are the respective version of the package. Then inside the extracted folder “PhytoSFDM”, type the following command:
$ python setup.py install
If you do not have permission to write in the python distribution folder then use command sudo before the suggested installation lines (Unix like systems).
The example calculates all the model variants at a testing location in the Atlantic Ocean (47.5° N and 15.5° W). To run the example just type in a terminal:
or alternatively in an interactive python console you can import the example and run it by typing:
>>> import phytosfdm.Example.example as exmp >>> exmp.main()
To calculate one of the five model variants (Full, Immigration, Trait Diffusion, Fix Variance and Unsustained Variance) at a specific set of coordinates, one can import the required library in an interactive python console as:
>>> from phytosfdm.SizeModels.sizemodels import SM >>> Lat = 47.5 >>> Lon = -15.5 >>> RBB = 2.5 >>> SM1 = SM(Lat,Lon,RBB,"Imm")
where SM is the class that contains all the methods to calculate a specific size model, Lat and Lon are Latitude (-90 to 90 degrees, North positive) and Longitude (-180 to 180 degrees, East positive), RBB is the range of the bounding box (in degrees) for averaging the environmental forcing variables and SM1 is an object that contains the results of the size model with an immigration treatment. After execution the results of the model can be accessed by:
In the multidimensional array “SM1.outvariables” the first dimension is time (e.g. 3650 days if the model is run with default parameters of 10 years) and the second dimension contains the state variables, for the full model, or the state variables and the dummy variables for the aggregate models.
To access all attribute values of the class instance “SM1” one can type in an interactive python console:
To modify the default parameter values, for example, the user can call a new class instance with a tuple list with the parameter name and its new value:
>>> SM2 = SM(Lat,Lon,RBB,"Imm",defaultParams=False,ListParams=[("timeyears",5),("muP",1.5])
Please refer to the documentation inside of the class and its methods for further details.
Version 1.1.xThree new methods were added to PhytoSFDM. Two in the class SM, which allow to a) test the effect of fixing phytoplankton mean cell size in the two size dependent processes (grazing or nutrient uptake), and b) test the effect of fixing the change over time of simultaneously the mean size and the size variance. The test using the new methods can be executed as:
>>> SM3 = SM(Lat,Lon,RBB,"Imm_NuGTest",fixs=np.log(3),fixsg=True)
>>> SM4 = SM(Lat,Lon,RBB,"Imm_NuGTest",default=False,listparams=[('N0', 10.)],fixn0x=True)
>>> SM5 = SM(Lat,Lon,RBB,"Imm_FixMeanVar",default=False,listparams=[('L_var0',5.0),('L_mean0',np.log(3)) ],fixn0x=True)
The last new method on version 1.1.x allow to visualize the four environmental forcing variables at the selected location:
>>> from phytosfdm.EnvForcing.envforcing import ExtractEnvFor >>> mld = ExtractEnvFor(Lat,Lon,RBB,'mld') >>> mld.selected_area_plots()
I would like to thank Jorn Bruggeman for his valuable contribution to an earlier version of the size-based model and my colleagues, Gunnar Brandt, S. Lan Smith and Agostino Merico for their continuous support and encouragement to complete this project.
Acevedo-Trejos, E., Brandt, G., Bruggeman, J. & Merico, A. Mechanisms shaping phytoplankton community structure and diversity in the ocean. Sci. Rep. 5, 8918 (2015).
Bruggeman, J. & Kooijman, S. A. L. M. A biodiversity-inspired approach to aquatic ecosystem modeling. Limnol. Oceanogr. 52, 1533–1544 (2007).
Bruggeman, J. Succession in plankton communities: A trait-based perspective. (2009).
Fasham, M., Ducklow, H. W. & Mckelvie, S. M. A nitrogen-based model of plankton dynamics in the oceanic mixed layer. J. Mar. Res. 48, 591–639 (1990).
Merico, A., Bruggeman, J. & Wirtz, K. A trait-based approach for downscaling complexity in plankton ecosystem models. Ecol. Modell. 220, 3001–3010 (2009).
Norberg, J. et al. Phenotypic diversity and ecosystem functioning in changing environments: a theoretical framework. Proc. Natl. Acad. Sci. 98, 11376–81 (2001).
Terseleer, N., Bruggeman, J., Lancelot, C. & Gypens, N. Trait-based representation of diatom functional diversity in a plankton functional type model of the eutrophied Southern North Sea. Limnol. Oceanogr. 59, 1–16 (2014).
Wirtz, K. W. Mechanistic origins of variability in phytoplankton dynamics: Part I: niche formation revealed by a size-based model. Mar. Biol. 160, 2319–2335 (2013).
Wirtz, K. W. & Sommer, U. Mechanistic origins of variability in phytoplankton dynamics. Part II: analysis of mesocosm blooms under climate change scenarios. Mar. Biol. 160, 2503–2516 (2013).
Wirtz, K. W. & Eckhardt, B. Effective variables in ecosystem models with an application to phytoplankton succession. Ecol. Modell. 92, 33–53 (1996).
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