Environmental Distance

openModeller id: ENVDIST

Current version: 0.5    Developer(s): Danilo J. S. Bellini, Renato De Giovanni

Accepts Categorical Maps: no

Requires absence points: no

Author(s): Mauro E. S. Munoz, Renato De Giovanni, Danilo J. S. Bellini


Generic algorithm based on environmental dissimilarity metrics. When used with the Gower metric and maximum distance 1, this algorithm should produce the same result of the algorithm known as DOMAIN.


Carpenter G, Gillison AN, Winter J (1993) DOMAIN: A flexible modeling procedure for mapping potential distributions of animals and plants. Biodiversity and Conservation 2: 667-680. Farber O & Kadmon R 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling 160: 115–130.



openModeller id: DistanceType

Metric used to calculate distances: 1=Euclidean, 2=Mahalanobis, 3=Manhattan/Gower, 4=Chebyshev

Data type: integer  Domain: [1.0, 4.0]  Typical value: 1

Nearest 'n' points

openModeller id: NearestPoints

Nearest 'n' points whose mean value will be the reference when calculating environmental distances. When set to 1, distances will be measured to the closest point, which is the same behavior of the previously existing minimum distance algorithm. When set to 0, distances will be measured to the average of all presence points, which is the same behavior of the previously existing distance to average algorithm. Intermediate values between 1 and the total number of presence points are now accepted.

Data type: integer  Domain: [0.0, 1000.0]  Typical value: 1

Maximum distance

openModeller id: MaxDistance

Maximum distance to the reference in the environmental space, above which the conditions will be considered unsuitable for presence. Since 1 corresponds to the biggest possible distance between any two points in the environment space, setting the maximum distance to this value means that all points in the environmental space will have an associated probability. The probability of presence for points that fall within the range of the maximum distance is inversely proportional to the distance to the reference point (linear decay). The only exception is when the maximum distance is 1 and the metric is Mahalanobis, which will produce probabilities following the chi-square distribution.

Data type: real  Domain: [0.0, 1.0]  Typical value: 0.1

Sample models

The following images show models in the environmental space (temperature x precipitation) generated with the same input (Thalurania furcata boliviana localities dataset) but with different parameters. Notice the different shapes produced by each metric:

fig. 1: euclidean distance to nearest point, max distance = 0.05fig. 2: gower distance to nearest point, max distance = 0.05fig. 3: mahalanobis distance to nearest point, max distance = 0.05
fig. 4: mahalanobis distance to the centroid, max distance = 0.3fig. 5: gower distance to the centroid, max distance = 0.15fig. 6: euclidean distance to the centroid of the 10 nearest points, max