A new tool for fundamental niche modelling

1

• A new tool for fundamental niche modelling
• Renato De Giovanni
• Centro de Referência em Informação Ambiental, CrIA

2
openModeller

• Scope and definition
• Fundamental niche modelling
• History of openModeller
• Motivation and features
• Architecture overview
• Interfaces and additional tools
• Algorithms
• Future plans

3
Definition

• openModeller is an open source C library
  completely
• dedicated to static spatial distribution
  modelling.

Applications
Biology Fundamental niche modelling. Geology
? Demography ? Others ?
4
Fundamental niche modelling
Model creation step
Occurrence points (geographical space)
Occurrence points (environmental space)
Environmental layers
5
Fundamental niche modelling
Model creation step
Distribution Model
Occurrence points (environmental space)
Algorithm
6
Distribution map projection step
Fundamental niche modelling
Distribution Model
Distribution map
Algorithm
Environmental layers
7
openModellers 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).

8
Main Motivation

• Facilitate and speed up modelling tasks, offering
  at the same time a homogeneous environment to
  carry out experiments with different algorithms.

Main 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.

9
Architecture overview
10
Interfaces 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)

11
Console interface
gtgt om_console request.txt WKT Coord System
Species file Species Map Mask Output
map Output mask Output format Output
file Algorithm Parameter
12
Console interface
13
Console interface
14
Tool for visualizing maps
gtgt om_viewer -r request.txt
15
Tool for visualizing models
gtgt om_niche request.txt
16
Web Interface
17
Web Interface
18
Graphical User Interface
19
Graphical User Interface
20
Graphical User Interface
21
Development 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.

22
Algorithms Building models
Sampler gives the algorithm vectors of
environmental values from a set of occurrence
points Ex 20, 115 mm, 22, 100 mm
open Modeller
Algorithm
API
Algorithm uses the values to build a distribution
model and stores an internal representation of
it.
23
Algorithms Generating distribution maps
For each cell of the resulting map,
openModeller asks the probability of presence
sending the vector of environmental values as a
parameter. Ex probability for 30, 90 mm ?
open Modeller
Algorithm
Algorithm answers with a probability of
presence. Ex prob F( 30, 90 mm ) 0.8
24
Algorithms

• Bioclim
• Climate Space Model (Broken Stick cutoff method)
• GARP (incl. best subset procedures)
• Distance algorithms
• Distance to average
• Minimum distance

25
Algorithms - Bioclim

• Assumes normal distribution for each
  environmental variable.
• Envelopes are represented by the interval m -
  cs, m cs, 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. 2 cutoff 0.99
fig. 1 cutoff 0.674
26
Algorithms - 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
27
Algorithms GARP with Best subsets procedure

• 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.

fig. 1 sample model
28
Algorithms 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
29
Algorithms 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
30
Use case Byrsonima subterranea Brad. Markgr.
original point
4 new points
31
Scope 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.

32
Future 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.

33
New version of the graphical interface
34
Institutions People
Mauro Muñoz, Marinez F. Siqueira,
Sidnei de Souza, Renato De Giovanni
Rubens R. Fonseca, Fabiana Santana,
and others
Lúbia Vinhas, Alexandre C. Jardim
and others
Tim Sutton, Peter Brewer
Ricardo S. Pereira, Kevin Ruland
Jens Oberender, Tom Elwertowsky
Voluntary work
35

• Thank you
• http// openmodeller . sf . net
• renato (at) cria . org . br