Online Resource 3: Data and analysis of list of AEMs and hydrodynamic drivers.
Method
From the list given in Online Resource 1, we selected those that can be qualified as being an AEM
according to our definition. For 42 models we were able to obtain data from experts to categorize
them. These categories relate to the modelling approach, environmental domain of the model (e.g.
shallow lake), the application domain (e.g. eutrophication), the types of analysis that are available,
availability and legal status of source code and executable, hydrodynamic aspects, spatial aspects, the
modelling framework, programming language, toolboxes and databases used and finally the type of
user interface to access the model (see for the full list in the Online Resource 5). We analysed the
frequency distribution of the models over different subcategories. Of the most intriguing outcomes, a
spider chart is created which can be found in the main text under section ‘Categorizing diversity’.
Results
Analysis type available
Simulate temporal dynamics 90%
Scenario analysis
71%
Sensitivity analysis
51%
Calibration
49%
Validation
43%
Uncertainty analysis
41%
Bifurcation analysis
16%
90% of the models for which we obtained data are dynamic and therefore allow to simulate temporal
dynamics. The remaining models are static and based on statistical relations. Over two out of three
models have tools for scenario evaluation. Half of the models have tools for sensitivity analysis and
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calibration. Validation and uncertainty analysis is implemented for over 40% of the models.
Bifurcation analysis, which technique is extensively employed in theoretical ecology is available for
one out of six of the AEMs that we analysed.
Application domain
Eutrophication
98%
Climate change
79%
Carbon cycle
68%
Fisheries
34%
Biodiversity loss 17%
Adaptive processes 8%
Eutrophication was the application domain of no less than 98% of the analysed models. Next comes in
decreasing order of importance climate change, carbon cycle, fisheries, biodiversity loss and adaptive
processes. This signals that the fields of biodiversity and evolution are not well covered by AEMs
although we know that eutrophication, climate change and fisheries can have severe consequences for
biodiversity and form an important selective force. The resulting changes in biodiversity and genetic
composition may have important feedbacks on ecosystem functioning.
Availability source code
Free on request
50%
Free download
36%
Not available
18%
Licence can be bought 7%
The majority of models that we analysed use an open source policy (free on request or free
download). For one out of six models, the source code is not available at all. For one out of seven
models a licence for the source code can be bought.
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Availability executable
Free through download
41%
Free through own compilation 36%
Free on request
33%
Not available
15%
Licence can be bought
12%
Free through web application 7%
For just over 40% of the analysed models the executable can be freely downloaded and for a slightly
smaller percentage of models the executable can be obtained for free through own compilation. For
one out of three models, the executable is freely provided on request. For one out of six models, the
executable is not available. In less than one out of ten cases, it can be bought. For three of the forty
four models that we analysed, free access to the executable is provided through a web application.
Copyright policy source
GPL
26%
Proprietary
26%
LGPL
21%
Just less than half of the models is distributed using a GPL (General Public Licence) or a LGPL
(Lesser General Public Licence) copyright policy for the model source. One out of four models is
proprietary with respect to the source code.
Copyright policy executable
Free to use and distribute
50%
Free to use but not distribute 22%
Proprietary
16%
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For half of the analysed models, users are free to use and distribute the executable. For 22% of the
models, this is prohibited. One out of six models is proprietary with respect to the executable.
Database definition of model available
DATM
14%
The idea to implement models in a database format was only recently launched. It is therefore not
surprising that only one out of seven of the analysed models is implemented in this way.
Environmental domain
Shallow lakes
81%
Reservoirs
52%
Deep lakes
51%
Rivers
43%
Estuaries
39%
Seas
33%
Ditches/canals
33%
Oceans
31%
Wetlands
29%
Catchments
22%
Global
22%
Coastal
10%
Despite the fact that the AEMON community that provided the analysed models is traditionally biased
towards lake modelling, the 42 analysed models cover about every aquatic habitat, with even 22% of
the models claiming global applicability. A deeper analysis of the extent to which models of different
aquatic habitats are currently linked or could potentially be linked is a very interesting one.
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Hydrodynamic driver
Simple 0D mass balance
34%
Build-in hydrodynamic model 32%
GOTM
20%
GETM
15%
MOM
13%
FVCOM
8%
GLM
8%
NEMO
8%
ROMS
8%
DUFLOW
7%
Delft3D-FLOW
5%
Delft3D-WAVE
5%
SOBEK
5%
Coherens
3%
DYRESM
3%
ELCOM
3%
Mike11
3%
Mike21
3%
Mike-SHE
3%
PERSIST
3%
Simstrat
3%
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One third of the analysed models contains a simple 0D water balance and another (partly overlapping)
third contains a built-in hydrodynamic or hydrological driver. Among the external drivers, GETM,
GOTM and MOM are most in use. Next comes a list of no less than 16 hydrodynamic and
hydrological drivers, each used by a few or even a single model in our set.
Hydro-eco process linker
FABM
15%
DELWAQ
7%
DUFLOW
5%
MOSSCO
2%
ESMF
0%
Over the years several laudable initiatives have been taken to develop standardized interfaces between
hydrological drivers and process models. It is interesting to see that also at this level there is evolving
diversity. For instance, FABM is recently developed, while DELWAQ has been around for decades,
although it is available in open source only since 2013. Within the set of analysed models, the former
framework is most commonly used (by one out of six models).
Mathematical format
Partial differential equations 50%
Ordinary differential equations 48%
Difference equations
14%
Input-output relation
10%
Agent-based event driven
5%
Lattice differential equations
2%
The mathematical solution techniques for partial and ordinary differential equations are equally
important for implementing AEMs. Other formats in use are in decreasing order of popularity:
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difference equations, input-output relations, agent-based event driven and finally lattice differential
equations.
Model stored in repository
FABM process model repository
15%
DELWAQ process model repository 7%
DUFLOW process model repository 5%
Some modelling frameworks aim for hosting a suite of AEMs. Among the 42 models that we analysed
one out of four was available as part of either the FABM, DELWAQ or DUFLOW model repository.
Modelling approaches
Aquatic Ecosystem model
100%
Dynamical model
86%
Process-based model
81%
Biogeochemical model
79%
Mass balanced model
78%
Compartment model
76%
Complex dynamical model
76%
Stoichiometric model
62%
Spatially explicit model
59%
Competition model
52%
Consumer-resource model
50%
Food web model
50%
Community model
45%
NPZD model
26%
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Hydrodynamic model
24%
Individual-based community model 14%
Trait-based model
12%
Dynamic Energy Budget model
12%
Environmental niche model
11%
Hydraulic model
10%
Meta model
10%
Statistical model
7%
Optimization model
5%
Generalized Lotka-Volterra model
5%
Hydrological model
5%
Neural network model
5%
Structural equation model
5%
Physiologically structured model
2%
Regression model
2%
Minimal dynamical model
0%
We listed thirty types of modelling approaches to get a better insight in the nature of the 42 analysed
models. These categories are non-exclusive and not exactly defined. Yet, we believe they give an idea
of what are the dominant and what are the more rare approaches in use in aquatic ecosystems
modelling. By definition, all models reported here were qualified as being an AEM. Over 75% of
them were qualified as being dynamic, process-based, biogeochemical, mass-balanced,
compartmented and complex dynamical. Over 45% of them were qualified as being stoichiometric
and spatially explicit as well as being a competition, a consumer-resource, a food web and a
community model. About 25% of them was qualified a being of the NPZD type of model as well as
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being a hydrodynamical model. One out of seven of the analysed models contained individual-based
approaches, more specifically being an individual-based community model, a trait-based model or a
dynamic energy budget model. More rare were the following qualifications (in decreasing order of
importance): environmental niche model, hydraulic model, meta model, optimization model,
statistical model, generalized Lotka-Volterra model, hydrological model, neural network model,
physiologically structured model, structural equation model, and regression model. The low score of
statistical models, either based on regression or neural networks shows that this approach is not wellrepresented in our dataset. None of the model was scored as being a minimal dynamical model,
whereas this approach provides all the building blocks for process-based AEMs and conceptually can
form an AEM in itself (see figure 1). One could also argue the NPZD models could be qualified as
minimal dynamical models.
Modelling framework
R/deSolve
19%
FABM
15%
Web application tool 8%
DELWAQ
5%
DUFLOW
5%
Matlab
5%
AQUASIM
3%
Ecopath with Ecosim 3%
Stella
3%
ACSL
1%
GRIND for Matlab
1%
OSIRIS
1%
Mathematica
0%
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SENECA
0%
SMART
0%
VisSim
0%
More than two out of three models is developed within an existing modelling framework. R/deSolve
and FABM come out as the most used frameworks (19% and 15% respectively). Thereafter follows a
long list of frameworks in use, including Web application tools, DELWAQ, DUFLOW, Matlab,
Aquasim, Ecopath with Ecosim, Stella, ACSL, Grind for Matlab and OSIRIS. One can rightfully say
that the field of aquatic ecosystem modelling is quite scattered when it comes to the use of modelling
frameworks. This notion was one of the incentives for developing DATM (Mooij et al. 2014).
Numerical integrator method or library
Euler
49%
Runge Kutta 4th order
22%
R integrators
15%
Matlab integrators
7%
Numerical recipes integrators
7%
DELWAQ integrators
5%
DUFLOW integrators
5%
Odepack integrators
5%
ACSL integrators
1%
All AEMs that are formulated as differential equations require numerical integration for simulating
temporal dynamics. Except for Euler integration and fixed time step Runge Kutta, these integrators
are quite complex and use of existing and well-tested routines from software libraries is therefore
highly recommended. In decreasing popularity, the following integrators are in use in our set of
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models: R/deSolve, Matlab, Numerical recipes, DELWAQ, DUFLOW, Odepack and ACSL
integrators.
Parameter database used
BLOOM parameter database
2%
Ecopath with Ecosim parameter database 2%
Parameter prior database
2%
PROTECH parameter database
2%
AQUATOX parameter database
0%
DEB parameter database
0%
We consider it good modelling practice to use databases of parameters. Currently, these databases are
not widely used however. In fact we identified four databases in use, each by one model.
Programming language
FORTRAN
50%
C
15%
Delphi
15%
R
13%
C++
11%
Matlab
10%
Python
9%
Visual Basic
9%
DUPROL
6%
Through Graphical User Interface (e.g. Stella) 6%
JAVA
3%
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ACSL
1%
GRIND for MATLAB
0%
Mathematica
0%
With 50%, FORTRAN is the dominant programming language for implementing AEMs. Next comes
C or C++ (together 26%), followed by a long list of programming languages, including Delphi, R,
Matlab, Python, Visual Basic, Duprol, JAVA, ACSL or GRIND for MATLAB. One can rightfully say
that the field of aquatic ecosystem modelling is quite scattered regarding the use of programming
languages. This notion was one of the incentives for developing DATM.
Programming style
Procedural
68%
Scripting
29%
Object-oriented 26%
68% of the models for which we obtained data are written in a traditional procedural programming
style (e.g. procedural FORTRAN or C). 29% is written in an object-oriented style (e.g. C++ or
Delphi) and 26% in a scripting language (e.g. R or Python).
Spatial configuration
Box model
54%
Network
26%
Cubic grid
24%
Curvilinear
23%
Flexible mesh
16%
Finite Element 15%
Triangular mesh 11%
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Polar
5%
Just over half of the models for which we obtained data can be run as box model. With respect to
spatially explicit models, various spatial configurations are employed. In decreasing popularity these
are: networks, cubic grid, curvilinear, flexible mesh, finite element, triangular mesh and polar.
Spatial dimension
0D
51%
1D vertical
39%
2D horizontal
32%
3D
29%
1D horizontal
28%
Networks of linear waters 22%
2D vertical
20%
Half of the models for which we obtained data can be run in a 0D mode. Next comes a 1D vertical
model, typically with a focus on stratification. About one out of three models can be run in a 2D
horizontal, a 3D or a 1D horizontal mode. One out of five models can be run as a network of linear
waters or in a 2D vertical mode.
Toolboxes used
R-packages
13%
Matlab toolboxes
4%
Numerical recipes
4%
Libreoffice toolboxes
3%
GRIND toolboxes
1%
LIN/EISPACK libraries 0%
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We consider it good modelling practice to use existing toolboxes for tasks such as numerical
integration, statistical analysis and graphical presentation. R-packages come out as at the most used
(one out of seven models). Numerical recipes, which was THE modeller's handbook of the 90’s of the
past century seems to be fading away, although some of the methods in more recent toolboxes may
actually contain code from it. This certainly applies for LINPACK/EISPACK, which were never
mentioned.
User interface
Graphical User Interface 63%
Console
42%
Excel
13%
Almost two out of three of the models for which we obtained data can be accessed through a graphical
user-interface. Just over 40% is controlled from a command line on a console. An interesting
development is the use of Excel for specifying model input and inspecting model output. This
approach is used by one out of seven of the models for which we obtained data. We observed that
model access through Excel is particularly liked by inexperienced users, because they know the
software and have access to it.
References
Mooij, WM, Brederveld, RJ, De Klein, JJM, DeAngelis, DL, Downing, AS, Faber, M, Gerla, DJ,
Hipsey, MR, t Hoen, J, Janse, JH, Janssen, ABG, Jeuken, M, Kooi, BW, Lischke, B, Petzoldt, T,
Postma, L, Schep, SA, Scholten, H, Teurlincx, S, Thiange, C, Trolle, D, Van Dam, AA, Van Gerven,
LPA, Van Nes, EH and Kuiper, JJ 2014. Serving many at once: How a database approach can create
unity in dynamical ecosystem modelling. Environ Modell Softw 61:266-273.
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