gdm (version 1.6.0-4)

gdm.transform: Transform Environmental Data Using a Fitted Generalized Dissimilarity Model

Description

This function transforms geographic and environmental predictors using (1) the fitted functions from a model object returned from gdm and (2) a data frame or raster object containing predictor data for a set of sites.

Usage

gdm.transform(model, data, filename = "", ...)

Value

gdm.transform returns either a data frame with the same number of rows as the input data frame or a SpatRaster, depending on the format of the input data. If the model uses geographic distance as a predictor the output object will contain columns or layers for the transformed X and Y values for each site. The transformed environmental data will be in the remaining columns or layers.

Arguments

model

A gdm model object resulting from a call to gdm.

data

Either (i) a data frame containing values for each predictor variable in the model, formatted as follows: X, Y, var1, var2, var3, ..., varN or (ii) a terra object SpatRaster with one layer per predictor variable used in the model, excluding X and Y (rasters for x- and y-coordinates are built automatically from the input rasters if the model was fit with geo=TRUE). The order of the columns (data frame) or raster layers (SpatRaster) MUST be the same as the order of the predictors in the site-pair table used in model fitting. There is currently no checking to ensure that the order of the variables to be transformed are the same as those in the site-pair table used in model fitting. If geographic distance was not used as a predictor in model fitting, the x- and y-columns need to be removed from the data to be transformed. Output is provided in the same format as the input data.

filename

character. Output filename for rasters. When provided the raster layers are written to file directly.

...

additional arguments to pass to terra predict function.

References

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 & Distributions 13, 252-264.

Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modeling of biodiversity: Mapping the genomic landscape of current and future environmental adaptation. Ecology Letters 18: 1-16

Examples

# start with the southwest data set
# grab the columns with xy, site ID, and species data
sppTab <- southwest[, c("species", "site", "Lat", "Long")]

##fit gdm using rasters
rastFile <- system.file("./extdata/swBioclims.grd", package="gdm")
envRast <- terra::rast(rastFile)

sitePairRast <- formatsitepair(sppTab, 2, XColumn="Long", YColumn="Lat",
                               sppColumn="species", siteColumn="site",
                               predData=envRast)
##remove NA values
sitePairRast <- na.omit(sitePairRast)

##fit raster GDM
gdmRastMod <- gdm(sitePairRast, geo=TRUE)

##raster input, raster output
transRasts <- gdm.transform(gdmRastMod, envRast)

# map biological patterns; increase maxcell if using large rasters
pcaSamp <- terra::prcomp(transRasts, maxcell = 1e4)

# note the use of the 'index' argument
pcaRast <- terra::predict(transRasts, pcaSamp, index=1:3)

# stretch the PCA rasters to make full use of the colour spectrum
pcaRast <- terra::stretch(pcaRast)

terra::plotRGB(pcaRast, r=1, g=2, b=3)