Generalized Dissimilarity Model - GDM


This functor predicts the beta-diversity patterns by using environmental predictors. For more details, see: Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13(3), 252–264. doi:10.1111/j.1472-4642.2007.00341.x


Name Type Description
Input Occurences csv file Csv file with species occurences (colunms sp, x, y).
Predictors Map Type Map of predictor variables in geoTiff format.
Output folder Folder Folder to save outputs.
Input Mask Map Filename Type The filename of the shape of area of study.
Hexagons As Sample Units Boolean Value Type Use hexagons as sample units to generate the species data table by location (recommended when data is sparse and with gaps).
Hexagon Size Real Value Type Value that define size of sample units.
Minimum Number Of Samples Real Value Type Minimum number of samples in hexagon
Project In Another Scenario Boolean Value Type Project GDM in another scenario (future or past)
Folder Scenarios Raster Folder Folder with rasters for project in another scenario. The rasters should be named in the same way of predictors.
Use Geographical Distances Boolean Value Type Use geographical distances as predictors
Classify GDM Boolean Value Type Classify areas in GDM by unsupervised classification
Number Of Classes Real Value Type Number of classes in classification


Name Type Description
Explained of Model Real Value Type Percentage of explanation of model
Map Map Type Map of GDM prediction



First, this functor verifies whether there is a valid input in “Possible Map 1”; if not, it chooses “Possible Map 2” as input. It is a convenient way to provide an alternate categorical map in case an execution pipeline does not return a valid data. If “Possible Map 2” is not set, the functor will return a void output.

Internal Name