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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