ctmm (version 0.3.0)

occurrence: Calculate a Kriged occurrence disribution estimate

Description

This function calculates an occurrence distribution from telemetry data and a continuous-time movement model.

Usage

occurrence(data,CTMM,H=diag(0,2),res.time=20,res.space=1000,grid=NULL,
cor.min=0.5,dt.max=NULL)

Arguments

data

2D timeseries telemetry data represented as a telemetry object.

CTMM

A ctmm movement model from the output of ctmm.fit.

H

Optional additional banwidth matrix for future use.

res.time

Number of temporal grid points per median timestep.

res.space

Number of grid points along each axis.

grid

Optional grid specification with columns labeled x and y. Not yet supported.

cor.min

Minimum amount of correlation required between locations for Kriging.

dt.max

Maximum time interval allowed between locations for Kriging.

Value

• 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.

References

C. H. Fleming and W. F. Fagan and T. Mueller and K. A. Olson and P. Leimgruber and J. M. Calabrese. Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data.

See Also

akde, raster.UD

Examples

# Load package and data
library(ctmm)
data(buffalo)
cilla <- buffalo[[1]]

# Fit a continuous-velocity model with tau ~ c(10 days, 1 hour)
# see help(variogram.fit)
GUESS <- ctmm(tau=c(10*24*60^2,60^2))
FIT <- ctmm.fit(cilla,GUESS)

# Compute occurence distribution
UD <- occurrence(cilla,FIT)

# Plot data with UD
plot(cilla,UD=UD,col.level=NA)