| get_hr | R Documentation | 
These functions extract ‘home range’ estimates from SpatRaster (or raster) objects that describe the intensity of movements within an area (from example from pf_kud).
get_hr_prop(
  x,
  prop = 0.5,
  plot = TRUE,
  add_raster = list(),
  add_contour = list(),
  ...
)
get_hr_core(x, plot = TRUE, add_raster = list(), add_contour = list(), ...)
get_hr_home(x, plot = TRUE, add_raster = list(), add_contour = list(), ...)
get_hr_full(x, plot = TRUE, add_raster = list(), add_contour = list(), ...)
| x | A  | 
| prop | For  | 
| plot | A logical variable that defines whether or not to plot the home range. | 
| add_raster,add_contour,... | Plot customisation options.  | 
Animal home ranges are widely quantified as 'the smallest subregion that accounts for a specified proportion, p, of [the animal's] total distribution' (Jennrich and Turner 1969, page 232). In line with this approach, get_hr_prop extracts the region within a frequency distribution of space use (i.e., UD) that is enclosed by a specified proportion (prop) contour. Following the most widely used adopted conventions, three additional wrapper functions facilitate the extraction of core, home and full ranges:
get_hr_core extracts the ‘core range’ as the region enclosed by the 50 percent contour of the UD (prop = 0.50);
get_hr_home extracts the ‘home range’ as the 95 percent contour of the UD (prop = 0.95);
get_hr_full extracts the ‘full’ range as the boundaries of the UD (prop = 1.00);
These functions are simple wrappers for raster.vol. They differ from functions in the adehabitatHR package (namely getverticeshr) in that they are designed to input and output raster objects.
The functions return a raster. Cells with a value of one are inside the specified range boundaries; cells with a value of zero are beyond range boundaries.
Edward Lavender
Jennrich, R. I. and Turner, F. B. (1969). Measurement of non-circular home range. Journal of Theoretical Biology, 22, 227–237.
#### Define an example UD
# We will use particles sampled by a particle filtering algorithm
# ... to create a UD:
particles <- pf_simplify(dat_dcpf_histories,
  summarise_pr = max,
  return = "archive"
)
# Define grids for UD estimation
map <- dat_dcpf_histories$args$bathy
habitat <- kud_habitat(map, plot = FALSE)
# Define UD as a raster
ud <- pf_kud_2(particles,
  bathy = map, grid = habitat,
  estimate_ud = kud_around_coastline,
  plot = FALSE
)
#### Plot UD and home range estimators
pp <- par(mfrow = c(2, 2))
prettyGraphics::pretty_map(add_rasters = list(x = ud), main = "UD")
get_hr_full(ud, main = "Full range")
get_hr_home(ud, main = "Home range")
get_hr_core(ud, main = "Core range")
par(pp)
#### Extract custom ranges with get_hr_prop()
get_hr_prop(ud, prop = 0.25)
get_hr_prop(ud, prop = 0.10)
get_hr_prop(ud, prop = 0.05)
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