Generalized Dissimilarity Model - GDM

Description

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

Inputs

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

Outputs

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

Group

BioDinamica

Notes

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