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

This function calculates an occurrence distribution from `telemetry`

data and a continuous-time movement model.

1 | ```
occurrence(data,CTMM,H=0,res.time=10,res.space=10,grid=NULL,cor.min=0.5,dt.max=NULL)
``` |

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

`CTMM` |
A |

`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 |

`cor.min` |
Location correlation threshold for skipping gaps. |

`dt.max` |
Maximum absolute gap size (in seconds) for Kriging interpolation, alternative to |

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.

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`

.

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.

C. H. Fleming.

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. Kálmán filters for continuous-time movement models. Ecological Informatics, 40, 8-21 (2017).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# 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)
``` |

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