od: Occurrence Distribution

In amt: Animal Movement Tools

Occurrence Distribution

Description

od is a wrapper around ctmm::occurrence. See help(ctmm::occurrence) for more details. rolling_od estimates occurrence distributions for a subset of a track.

Usage

rolling_od(x, ...)

## S3 method for class 'track_xyt'
rolling_od(
  x,
  trast,
  model = fit_ctmm(x, "bm"),
  res.space = 10,
  res.time = 10,
  n.points = 5,
  show.progress = TRUE,
  ...
)

od(x, ...)

## S3 method for class 'track_xyt'
od(x, trast, model = fit_ctmm(x, "bm"), res.space = 10, res.time = 10, ...)

Arguments

x	[track_xyt] A track created with make_track that includes time.
...	Further arguments, none implemented.
trast	⁠[SpatRaster]⁠ A template raster for the extent and resolution of the result.
model	⁠[An output of fit_ctmm]⁠ The autocorrelation model that should be fit to the data. bm corresponds to Brownian motion, ou to an Ornstein-Uhlenbeck process, ouf to an Ornstein-Uhlenbeck forage process.
res.space	⁠[numeric(1)=10]⁠ Number of grid point along each axis, relative to the average diffusion (per median timestep) from a stationary point. See also help(ctmm::occurrence).
res.time	⁠[numeric(1)=10]⁠ Number of temporal grid points per median timestep.
n.points	⁠[numeric(1)=5]⁠ This argument is only relevant for rolling_od and specifies the window size for the od estimation.
show.progress	⁠[logical(1)=TRUE]⁠ Indicates if a progress bar is used.

References

Fleming, C. H., Fagan, W. F., Mueller, T., Olson, K. A., Leimgruber, P., & Calabrese, J. M. (2016). Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology.

Examples

data(deer)
mini_deer <- deer[1:100, ]
trast <- make_trast(mini_deer)
md <- od(mini_deer, trast = trast)
terra::plot(md)

# rolling ud
xx <- rolling_od(mini_deer, trast)

amt documentation built on June 25, 2024, 1:14 a.m.