This function calculates an occurrence distribution from telemetry
data and a continuous-time movement model.
occurrence(data,CTMM,R=list(),SP=NULL,SP.in=TRUE,H=0,variable="utilization",res.time=10,
res.space=10,grid=NULL,cor.min=0.05,dt.max=NULL,buffer=TRUE,...)
Returns a UD
object containing the sampled grid line locations x
and y
, the probability density and cumulative distribution functions evaluated on the sampled grid locations PDF
& CDF
, the optional bandwidth matrix H
, and the area of each grid cell dA
.
A telemetry
object or list of telemetry
objects.
A ctmm
movement model, as from the output of ctmm.select
, or a list of ctmm
objects.
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
.
Optional additional bandwidth matrix for future use.
Either "utilization"
or "revisitation"
. Only utilization is accurately estimated.
Number of temporal grid points per median timestep.
Number of grid points along each axis, relative to the average diffusion (per median timestep) from a stationary point.
Optional grid specification via raster
, UD
, or list of arguments (See akde
for details).
Velocity correlation threshold for skipping gaps.
Maximum absolute gap size (in seconds) for Kriging interpolation. If left NULL
, the median of diff(data$t)
will be used.
Buffer the observation period, according to the minimum gap specified by cor.min
and dt.max
, to include more probable locations if possible.
Not used.
C. H. Fleming.
The arguments cor.min
or dt.max
are used to prevent the interpolation of large gaps, which would bias the estimate to more resemble the movement model than the data. Because cor.min
can produce an empty range with fractal movement models, the larger of the two rules is employed for interpolation.
If buffer=TRUE
, then the data are also extrapolated according to the minimum of the two rules (cor.min
and dt.max
) which is limited to cases where persistence of motion is modeled.
C. H. Fleming, W. F. Fagan, T. Mueller, K. A. Olson, P. Leimgruber, J. M. Calabrese, ``Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data'', Ecology, 97:3, 576-582 (2016) tools:::Rd_expr_doi("10.1890/15-1607.1").
C. H. Fleming, D. Sheldon, E. Gurarie, W. F. Fagan, S. LaPoint, J. M. Calabrese, ``Kálmán filters for continuous-time movement models'', Ecological Informatics, 40, 8-21 (2017) tools:::Rd_expr_doi("10.1016/j.ecoinf.2017.04.008").
akde
, raster,UD-method
# \donttest{
# Load package and data
library(ctmm)
data(buffalo)
Cilla <- buffalo$Cilla
GUESS <- ctmm.guess(Cilla,interactive=FALSE)
FIT <- ctmm.fit(Cilla,GUESS)
# Compute occurence distribution
UD <- occurrence(Cilla,FIT)
# Plot occurrence UD
plot(UD,col.level=NA)
# }
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