openmodeller a framework for biological/environmental modelling inter-american workshop on...
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openopenModellerModellerA framework for biological/environmental modellingA framework for biological/environmental modelling
Inter-American Workshop on Environmental Data AccessCampinas - SP, Brazil
March 2004
Species modellingSpecies modelling
prob = F(x1, ..., xN)
Tem
pera
ture
Precipitation
Example with prob > 0,8:
Species model can be seem as a function telling the probability of the occurrence of some species for a given environmental condition.
If we use xi to represent the i-th environment variable, then we have:
Building a modelBuilding a model
Occurrence points are the geographical coordinates where the species was found (or observed).
Pi = (Lati, Longi)
Tem
pera
ture
Precipitation
Building a modelBuilding a model
For each occurrence point we find the values assumed for the environment variables. Doing that we transform de geographical occurrence points in niche occurrence points.
Tem
pera
ture
Precipitation
Building a modelBuilding a model
Based on the niche occurrence points we build a niche model, F(X), through the application of some algorithm (ex: GARP, GAM, Bioclim, Artificial Neural Networks, etc).
Tem
pera
ture
Precipitation
Tem
pera
ture
Precipitation
Species distribution mapSpecies distribution map
The species distribution map is the result of the niche model application over some geographical region with known environment variables values. Thus, the species distribution map is a georeferenced map with species occurrence probabilities in its cells.
• Despite the terminology used here, strictly speaking, the distribution map shows the environmental similarities between distinct geographical regions according to the modelling algorithm metric. Using these similarities as probabilities of species occurrence must be done in a sensible way.
• Some factors as natural barriers and historical influences are not caught by the distribution map.
• The quality of the species occurrence data and the environment variable data are strictly related to the distribution map quality.
Warning!Warning!
Distribution map for Terminalia argenteausing GARP algorithm.
• Partnership: Embrapa/UnB/IBAMA/RBGE
• Internet downloaded: Missouri Botanical Garden
MotivationMotivation
• Read georeferenced environmental maps stored in different formats (GeoTiff, Arc/Info Grid, GXF, etc).
• Deal with different coordinate systems and projections to combine the different maps and the species occurrence points.
• Let the algorithm researchers concentrate in the algorithm development.
• Permit the execution of different algorithms with exactly the same input, so they can be compared.
Precipitation
Soil
Temperature
Environmental data
openModeller
BioclimNeural
NetworksGARP
Modellingalgorithms
Specimens
openopenModellerModeller
Precipitation
Soil
Temperature
Environmental data
openModeller
BioclimNeural
NetworksGARP
Modellingalgorithms
Specimens
Select the environment variables
Select the algorithm
Send the species occurrence data
Select the species’ name and the internet portals to be searched
DiGIRportal
DiGIRportal
openopenModellerModeller
DiGIRportal
DiGIRportal Precipitation
Soil
Temperature
Environmental data
openModeller
BioclimNeural
Networks GARP
Specimens
Modelling algorithms
ABCDportal
ABCDportal
openopenModellerModeller
openopenModeller Modeller client interfacesclient interfaces
openModeller
Desktop
Web
Soap
OR
OR
Library
OR ...
openopenModeller Modeller algorithm interfacealgorithm interface
openModeller Modellingalgorithm
Environmental values atspecies occurrence points.Ex: [20˚, 115 mm], [22˚, 100 mm]
Model Building
openopenModeller Modeller algorithm interfacealgorithm interface
openModellerModellingalgorithm
For each resulting map cell, openModeller asks forthe species occurrence probability.Ex: what is the probability for [30˚, 90 mm]
Species distribution map generation
Answer with the probability of occurrenceEx: prob = F( [30˚, 90 mm] ) = 0.8
The projectThe project
• The core is been developed in C++
• Uses GDAL and proj4 open source libraries
• Collaborative development
• Distributed under GPL license
Involved institutions:• CRIA – Centro de Referencia em Informação Ambiental• Poli USP - Escola Politécnica da Universidade de São Paulo• KU – Kansas University
