An introduction to data exchange protocols in TDWG Renato De Giovanni TDWG 2008.
A new tool for fundamental niche modelling Renato De Giovanni
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Transcript of A new tool for fundamental niche modelling Renato De Giovanni
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A new tool for fundamental niche modelling
Renato De Giovanni
Centro de Referência em Informação Ambiental, CrIA
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openModelleropenModeller
• Scope and definition
• Fundamental niche modelling
• History of openModeller
• Motivation and features
• Architecture overview
• Interfaces and additional tools
• Algorithms
• Future plans
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DefinitionDefinition
openModeller is an open source C++ library completely
dedicated to static spatial distribution modelling.
ApplicationsApplications
Biology: Fundamental niche modelling.
Geology ?
Demography ?
Others ?
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Fundamental niche modellingFundamental niche modelling
Occurrence points
(geographical space) Environmental layers
Occurrence points
(environmental space)
Tem
pera
ture
Precipitation
Model creation step
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Fundamental niche modellingFundamental niche modelling
Distribution Model
Algorithm
Occurrence points
(environmental space)
Tem
pera
ture
Precipitation
Tem
pera
ture
Precipitation
Model creation step
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Distribution map projection step
Distribution Model
Algorithm
Distribution map
Tem
pera
ture
Precipitation
Environmental layers
Fundamental niche modellingFundamental niche modelling
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openModeller’s historyopenModeller’s history
apr 2003: Initial design of a new modelling environment at CRIA as a natural consequence of previous experiences with other tools (DesktopGarp).
oct 2003: First working prototype as part of the speciesLink project (Fapesp).
dec 2003: Released all source code (sourceforge).
feb 2004: Partnership with BDWorld (CSM / GRID component).
apr 2004: Partnership with University of Kansas (GARP / BTRA).
jan 2005: Released first graphical user interface (Tim Sutton & Peter Brewer).
may 2005: Basis of a new thematic project funded by Fapesp (4y).
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Main MotivationMain MotivationFacilitate and speed up modelling tasks, offering at the same time a homogeneous environment to carry out experiments with different algorithms.
Main featuresMain features• Platform independent.• Enables the existence of multiple interfaces on top of it.• Accepts different formats of georeferenced maps.• Accepts different coordinate systems and projections for each
map and for the whole set of occurrence points.• Accepts different cell sizes and extents for each map.• Allows the different algorithms to use exactly the same input and
the same working environment, therefore enabling fair comparison between all results.
• Isolates algorithm logic from other issues related to maps, georeferencing, input and output formats, etc.
• Offers a collaborative and transparent environment for all interested developers.
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Architecture overviewArchitecture overview
openModeller
GARP
Bioclim
CSM
pluggablealgorithms
API
others...
APIConsole
interfaces
SOAPserver
SWIGwrapper
others...
drivers
pointspoints mapsmaps
(GDAL, proj4, etc)
(diff. formats)(diff. coord systems)
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Interfaces and additional toolsInterfaces and additional tools
• Command line / Console suite
– om_console
– om_viewer (X11)
– om_niche (X11)
• SWIG wrapper
– Python
• SOAP interface (prototype server and sample client)
• Web interface
• Graphical User Interface (Linux, Windows, Mac OS)
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Console interfaceConsole interface
>> om_console request.txt
WKT Coord System =
Species file =
Species =
Map =
Mask =
Output map =
Output mask =
Output format =
Output file =
Algorithm =
Parameter =
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Console interfaceConsole interface
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Console interfaceConsole interface
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Tool for visualizing mapsTool for visualizing maps
>> om_viewer -r request.txt
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Tool for visualizing modelsTool for visualizing models
>> om_niche request.txt
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Web InterfaceWeb Interface
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Web InterfaceWeb Interface
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Graphical User InterfaceGraphical User Interface
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Graphical User InterfaceGraphical User Interface
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Graphical User InterfaceGraphical User Interface
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Development of algorithmsDevelopment of algorithms
• Metadata definitions (name, version, author, description, bibliographic references, parameters).
• Method to initialize the algorithm.
• Method to generate the model.
• Method to calculate the probability of occurrence given a certain vector of environmental values.
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Algorithms: Building modelsAlgorithms: Building models
openModeller
Algorithm
API
Sampler gives the algorithm vectors of environmental values from a set of occurrence points:Ex: [20˚, 115 mm], [22˚, 100 mm]
Algorithm uses the values tobuild a distribution model and stores an internal representation of it.
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Algorithms: Generating distribution mapsAlgorithms: Generating distribution maps
openModeller
Algorithm
For each cell of the resulting map, openModellerasks the probability of presence sending thevector of environmental values as a parameter.
Ex: probability for [30˚, 90 mm] ?
Algorithm answers with a probability of presence.
Ex: prob = F( [30˚, 90 mm] ) = 0.8
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AlgorithmsAlgorithms
• Bioclim
• Climate Space Model (Broken Stick cutoff method)
• GARP (incl. best subset procedures)
• Distance algorithms
– Distance to average
– Minimum distance
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Algorithms - BioclimAlgorithms - Bioclim
• Assumes normal distribution for each environmental variable.
• Envelopes are represented by the interval [m - c*s, m + c*s], where 'm' is the mean; 'c' is the cutoff parameter; and 's' is the standard deviation.
• Besides the envelope, each environmental variable has additional upper and lower limits taken from the maximum and minimum values related to the set of occurrence points.
• Points are classified as: suitable, marginal or unsuitable.
fig. 1: cutoff = 0.674 fig. 2: cutoff = 0.99
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Algorithms - GARPAlgorithms - GARP
• Genetic Algorithm for Rule-set Production: models are represented by a set of rules generated by a genetic algorithm.
• Non-deterministic: produces a different model each time the algorithm is run.
fig. 1: model 1 fig. 2: model 2 fig. 3: model 3
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Algorithms – GARP with Best subsets procedureAlgorithms – GARP with Best subsets procedure
fig. 1: sample model
• Runs several GARP models and chooses the best ones according to omission and commission errors.
• Resulting model is the overlapping of models that were selected in the previous step.
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Algorithms – distance to averageAlgorithms – distance to average
• Normalizes environmental values and parameter.
• Calculates the mean point in environmental space considering all presence points.
• Probabily of presence is proportional to the Euclidean distance from the average point (linear decay).
• Parameter determines the maximum accepted distance.
fig. 1: parameter = 0.1 fig. 2: parameter = 0.3
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Algorithms – Minimum distanceAlgorithms – Minimum distance
• Normalizes environmental values and parameter.
• Probabily of presence is proportional to the Euclidean distance from the closest point (linear decay).
• Parameter determines the maximum accepted distance.
fig. 1: parameter = 0.05 fig. 2: parameter = 0.1
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Use case – Use case – Byrsonima subterraneaByrsonima subterranea Brad. & Markgr. Brad. & Markgr.
= original point
= 4 new points
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Scope issues & known limitationsScope issues & known limitations
• Works only with static models – dynamic modelling is currently outside the scope of this tool.
• None of the algorithms can handle categorical maps (although the library is already prepared to deal with them).
• None of the algorithms can handle absence points (except GARP), and none of the high level interfaces is prepared to receive absence points as an additional parameter.
• Works only with latitude & longitude – not prepared to produce models considering three dimensions.
• Needs more documentation.
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Future plansFuture plans
• Implementation of other algorithms: neural nets, cellular automata, GLM, GAM, GRASP, Domain, Maximum entropy…
• Development of new components to help on pre-processing and post-analysis.
• Finalize Web and SOAP interfaces.
• Develop SWIG interfaces for other programming languages.
• Produce more documentation.
• Implementation of a new and advanced graphical user interface.
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New version of the graphical interfaceNew version of the graphical interface
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Institutions & PeopleInstitutions & People
Tim Sutton, Peter Brewer
Ricardo S. Pereira, Kevin Ruland
Jens Oberender, Tom Elwertowsky
Mauro Muñoz, Marinez F. Siqueira,
Sidnei de Souza, Renato De Giovanni
Lúbia Vinhas, Alexandre C. Jardim
Rubens R. Fonseca, Fabiana Santana,
and others
and others
Voluntary work
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Thank youThank you
http:// openmodeller . sf . net
renato (at) cria . org . br