These functions calculate individual and population-level autocorrelated kernel density home-range estimates from telemetry data and a corresponding continuous-time movement models.
akde(data,CTMM,VMM=NULL,R=list(),SP=NULL,SP.in=TRUE,variable="utilization",debias=TRUE,
weights=FALSE,smooth=TRUE,error=0.001,res=10,grid=NULL,...)pkde(data,UD,kernel="individual",weights=FALSE,ref="Gaussian",...)
Returns a UD object: a list with the sampled grid line locations r$x and r$y, the extent of each grid cell dr, the probability density and cumulative distribution functions evaluated on the sampled grid locations PDF & CDF, the optimal bandwidth matrix H, and the effective sample size of the data in DOF.H.
2D timeseries telemetry data represented as a telemetry object or list of objects.
A ctmm movement model from the output of ctmm.fit or list of objects.
An optional vertical ctmm object for 3D home-range calculation.
A named list of raster covariates if CTMM contains an RSF model.
SpatialPolygonsDataFrame object for enforcing hard boundaries.
Locations are assumed to be inside the SP polygons if SP.in=TRUE and outside of SP if SP.in=FALSE.
Not yet supported.
Debias the distribution for area estimation (AKDEc).
"Smooth" out errors from the data.
Optimally weight the data to account for sampling bias (See bandwidth for akde details).
Target probability error.
Number of grid points along each axis, relative to the bandwidth.
Optional grid specification via raster, UD, or list of arguments (See `Details' below).
Arguments passed to akde, bandwidth, and mean.ctmm.
A list of individual UD objects corresponding to data.
Bandwidths are proportional to the individual covariances if kernel="individual" or to the population covariance if kernel="population".
Include non-Gaussian overlap corrections if ref="AKDE" and weights=TRUE.
C. H. Fleming and K. Winner.
For weighted AKDE, please note additional ... arguments passed to bandwidth, which can have a large impact on computation time in certain cases.
When feeding in lists of telemetry and ctmm objects, all UDs will be calculated on the same grid. These UDs can be averaged with the mean.UD command.
If a UD or raster object is supplied in the grid argument, then the estimate will be calculated on the same grid. Alternatively, a list of grid arguments can be supplied, with any of the following components:
rA list with vectors x and y that define the grid-cell midpoints.
drA vector setting the x and y cell widths in meters. Equivalent to res for raster objects.
extentThe \(x\)-\(y\) extent of the grid cells, formatted as from the output of extent.
align.to.originLogical value indicating that cell midpoint locations are aligned to be an integer number of dr steps from the projection origin.
C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, ``Rigorous home-range estimation with movement data: A new autocorrelated kernel-density estimator'', Ecology, 96:5, 1182-1188 (2015) tools:::Rd_expr_doi("10.1890/14-2010.1").
C. H. Fleming, J. M. Calabrese, ``A new kernel-density estimator for accurate home-range and species-range area estimation'', Methods in Ecology and Evolution, 8:5, 571-579 (2017) tools:::Rd_expr_doi("10.1111/2041-210X.12673").
C. H. Fleming, D. Sheldon, W. F. Fagan, P. Leimgruber, T. Mueller, D. Nandintsetseg, M. J. Noonan, K. A. Olson, E. Setyawan, A. Sianipar, J. M. Calabrese, ``Correcting for missing and irregular data in home-range estimation'', Ecological Applications, 28:4, 1003-1010 (2018) tools:::Rd_expr_doi("10.1002/eap.1704").
bandwidth, mean.UD, raster,UD-method, revisitation
# \donttest{
# Load package and data
library(ctmm)
data(buffalo)
DATA <- buffalo$Cilla
# calculate fit guess object
GUESS <- ctmm.guess(DATA,interactive=FALSE)
# in general, you should be running ctmm.select here instead of ctmm.fit
FIT <- ctmm.fit(DATA,GUESS)
# Compute akde object
UD <- akde(DATA,FIT)
# Plot data with AKDE
plot(DATA,UD=UD)
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