OpenModeller A framework for biological/environmental modelling Inter-American Workshop on...

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)

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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)

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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.

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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).

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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

Thank youThank you

http://openmodeller.cria.org.br

mauro@cria.org.br

Questions ?