openModeller id: VNG
Current version: 0.2 Developer(s): Renato De Giovanni
Accepts Categorical Maps: no
Requires absence points: no
Author(s): Renato De Giovanni
Algorithm used to create virtual niches using the first presence point as a reference for optimum environmental conditions. The niche is represented by a multivariate Gaussian distribution with the mean value based on the optimum conditions and a random standard deviation. Suitability is calculated by assuming independence between all variables, i.e., the final value is the product of the individual suitability for each variable. Individual suitabilities are calculated as the result of the Gaussian probability density function scaled by a factor to make the optimum condition correspond to 1. Standard deviations for each variable are randomly chosen within the range [x*S, S], where S is the standard deviation of the entire native region (calculated based on the background points) and x is the standard deviation factor parameter between 0 and 1.
renato [at] cria.org.br
Number of background points
openModeller id: NumberOfBackgroundPoints
Number of background points to be generated, which will be used to estimate the standard deviation of each variable in the area of interest.
Data type: integer Domain: [0.0, 10000.0] Typical value: 10000
Use absence points as background
openModeller id: UseAbsencesAsBackground
When absence points are provided, this parameter can be used to instruct the algorithm to use them as background points. This would prevent the algorithm to randomly generate them, also facilitating comparisons between different algorithms.
Data type: integer Domain: [0.0, 1.0] Typical value: 0
openModeller id: SuitabilityThreshold
Suitability threshold to get a binary niche. Use 1 if you want to keep the continuous niche.
Data type: real Domain: [0.0, 1.0] Typical value: 1.0
Standard deviation factor
openModeller id: StandardDeviationFactor
Factor (x) used to control the minimum limit of the random standard deviation for each variable. The random standard deviation will be a value between [x*S, S], where S is the standard deviation of the entire native region. Increase the factor to get larger niches, especially when using many environmental variables.
Data type: real Domain: [0.0, 1.0] Typical value: 0.0