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.

fig. 1: sample model |