`occurrence.Rd`

This function calculates an occurrence distribution from `telemetry`

data and a continuous-time movement model.

occurrence(data,CTMM,H=0,res.time=10,res.space=10,grid=NULL,cor.min=0.05,dt.max=NULL, buffer=TRUE)

data | 2D timeseries telemetry data represented as a |
---|---|

CTMM | A |

H | Optional additional bandwidth 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 via |

cor.min | Velocity correlation threshold for skipping gaps. |

dt.max | Maximum absolute gap size (in seconds) for Kriging interpolation. If left |

buffer | Buffer the observation period, according to the minimum gap specified by |

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.

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`

.

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) 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) doi: 10.1016/j.ecoinf.2017.04.008 .

C. H. Fleming.

Large gaps have a tendency to slow down computation and blow up the estimate. This 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.

Prior to `ctmm`

v0.5.6, `cor.min`

referred to the location correlation, with a default of 50%.
In `ctmm`

v0.5.6 and above, `cor.min`

refers to the velocity correlation, with a default of 5%.

# \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)# }