openModeller id: GARP_BS
Current version: 3.0.4 Developer(s): Ricardo Scachetti Pereira
Accepts Categorical Maps: no
Requires absence points: yes
Author(s): Anderson, R. P., D. Lew, D. and A. T. Peterson.
GARP is a genetic algorithm that creates ecological niche models for species. The models describe environmental conditions under which the species should be able to maintain populations. For input, GARP uses a set of point localities where the species is known to occur and a set of geographic layers representing the environmental parameters that might limit the species' capabilities to survive. This algorithm applies the Best Subsets procedure using the new openModeller implementation in each GARP run. Please refer to GARP single run algorithm description for more information about the differences between DesktopGarp and the new GARP implementation.
Anderson, R. P., D. Lew, and A. T. Peterson. 2003. Evaluating predictive models of species' distributions: criteria for selecting optimal models.Ecological Modelling, v. 162, p. 211 232.
Training Proportion
openModeller id: TrainingProportion
Percentage of occurrence data to be used to train models.
Data type: real Domain: [0.0, 100.0] Typical value: 50
Total Runs
openModeller id: TotalRuns
Maximum number of GARP runs to be performed.
Data type: integer Domain: [0.0, 10000.0] Typical value: 20
Hard Omission Threshold
openModeller id: HardOmissionThreshold
Maximum acceptable omission error. Set to 100% to use only soft omission.
Data type: real Domain: [0.0, 100.0] Typical value: 100
Models Under Omission Threshold
openModeller id: ModelsUnderOmissionThreshold
Minimum number of models below omission threshold.
Data type: integer Domain: [0.0, 10000.0] Typical value: 20
Commission Threshold
openModeller id: CommissionThreshold
Percentage of distribution of models to be taken regarding commission error.
Data type: real Domain: [0.0, 100.0] Typical value: 50
Commission Sample Size
openModeller id: CommissionSampleSize
Number of samples used to calculate commission error.
Data type: integer Domain: [1.0, oo] Typical value: 10000
Maximum Number of Threads
openModeller id: MaxThreads
Maximum number of threads of executions to run simultaneously.
Data type: integer Domain: [1.0, 1024.0] Typical value: 1
Max generations
openModeller id: MaxGenerations
Maximum number of iterations (generations) run by the Genetic Algorithm.
Data type: integer Domain: [1.0, oo] Typical value: 400
Convergence limit
openModeller id: ConvergenceLimit
Defines the convergence value that makes the algorithm stop (before reaching MaxGenerations).
Data type: real Domain: [0.0, 1.0] Typical value: 0.01
Population size
openModeller id: PopulationSize
Maximum number of rules to be kept in solution.
Data type: integer Domain: [1.0, 500.0] Typical value: 50
Resamples
openModeller id: Resamples
Number of points sampled (with replacement) used to test rules.
Data type: integer Domain: [1.0, 100000.0] Typical value: 2500
The following image shows a possible model in the environmental space (temperature x precipitation) generated with the Thalurania furcata boliviana localities dataset. It is possible to notice the overlapping of different rulesets.
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| fig. 1: sample model |