occurrence: Calculate a Kriged occurrence disribution estimate

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/krige.R

Description

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

Usage

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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.

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.

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.

Note

Large gaps have a tendency to blow up the estimate, and can be avoided with the cor.min or dt.max arguments.

In the case of coarse grids, the value of PDF in a grid cell actually corresponds to the average probability density over the entire rectangular cell.

Author(s)

C. H. Fleming.

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, DOI:10.1890/15-1607 (2016).

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).

See Also

akde, raster,UD-method

Examples

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# 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)

ctmm-initiative/ctmm documentation built on Feb. 17, 2018, 3:45 p.m.