ctmm (version 0.5.3)

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=0,res.time=10,res.space=10,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, relative to the average diffusion (per median timestep) from a stationary point.

grid

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

cor.min

Location correlation threshold for skipping gaps.

dt.max

Maximum absolute gap size (in seconds) for Kriging interpolation, alternative to cor.min.

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.

Details

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.

References

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).

C. H. Fleming, D. Sheldon, E. Gurarie, W. F. Fagan, S. LaPoint, J. M. Calabrese. K<U+00E1>lm<U+00E1>n filters for continuous-time movement models. Ecological Informatics, 40, 8-21 (2017).

See Also

akde, raster,UD-method

Examples

# NOT RUN {
# 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)
# }