Virtual Niche Generator

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]


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

Suitability threshold

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