hypervolume_project: Geographical projection of hypervolume for species...

In hypervolume: High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls

Geographical projection of hypervolume for species distribution modeling, using the hypervolume as the environmental niche model.

Description

Determines a suitability score by calculating the hypervolume value at each of a set of points in an input raster stack based on either a probability density estimation or inclusion test.

Note that projected values are not normalized and are not necessarily constrained to fall between 0 and 1.

Note also that additional arguments can be passed to this function to enable parallel operation (see ... below).

Usage

hypervolume_project(hv, rasters, type = "probability", verbose = TRUE,
                 ...)

Arguments

hv	An input hypervolume
rasters	A RasterStack with the same names as the dimension names of the hypervolume.
type	If 'probability', suitability scores correspond to probability density values estimated using hypervolume_estimate_probability; if 'inclusion', scores correspond to binary presence/absence values from calling hypervolume_inclusion_test.
...	Additional arguments to be passed to either hypervolume_estimate_probability or hypervolume_inclusion_test.
verbose	If TRUE, prints diagnostic and progress output.

Value

A raster object of same resolution and extent as the input layers corresponding to suitability values.

See Also

hypervolume_estimate_probability, hypervolume_inclusion_test

Examples

## Not run: 
  # load in lat/lon data
  data('quercus') 
  data_alba = subset(quercus, Species=="Quercus alba")[,c("Longitude","Latitude")]
  data_alba = data_alba[sample(1:nrow(data_alba),500),]
   
  # get worldclim data from internet
  require(maps)
  require(raster)
  climatelayers = getData('worldclim', var='bio', res=10, path=tempdir())
    
  # z-transform climate layers to make axes comparable
  climatelayers_ss = climatelayers[[c(1,12)]]
  for (i in 1:nlayers(climatelayers_ss))
  {
    climatelayers_ss[[i]] <- 
    	(climatelayers_ss[[i]] - cellStats(climatelayers_ss[[i]], 'mean')) / 
    	cellStats(climatelayers_ss[[i]], 'sd') 
  }
  climatelayers_ss = crop(climatelayers_ss, extent(-150,-50,15,60))
  
  # extract transformed climate values
  climate_alba = extract(climatelayers_ss, data_alba[1:300,])
  
  # compute hypervolume
  hv_alba <- hypervolume_gaussian(climate_alba)
  
  # do geographical projection
  raster_alba_projected_accurate <- hypervolume_project(hv_alba, 
                                      rasters=climatelayers_ss)
  raster_alba_projected_fast = hypervolume_project(hv_alba, 
                                      rasters=climatelayers_ss, 
                                      type='inclusion',
                                      fast.or.accurate='fast')
  
  # draw map of suitability scores
  plot(raster_alba_projected_accurate,xlim=c(-100,-60),ylim=c(25,55))
  map('usa',add=TRUE)
  
  plot(raster_alba_projected_fast,xlim=c(-100,-60),ylim=c(25,55))
  map('usa',add=TRUE)

## End(Not run)

hypervolume documentation built on May 17, 2022, 1:06 a.m.