occurrence | R Documentation |

This function calculates an occurrence distribution from `telemetry`

data and a continuous-time movement model.

```
occurrence(data,CTMM,R=list(),SP=NULL,SP.in=TRUE,H=0,variable="utilization",res.time=10,
res.space=10,grid=NULL,cor.min=0.05,dt.max=NULL,buffer=TRUE,...)
```

`data` |
A |

`CTMM` |
A |

`R` |
A named list of raster covariates if |

`SP` |
SpatialPolygonsDataFrame object for enforcing hard boundaries. |

`SP.in` |
Locations are assumed to be inside the |

`H` |
Optional additional bandwidth matrix for future use. |

`variable` |
Either |

`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 |

`...` |
Not used. |

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`

.

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%.

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) \Sexpr[results=rd]{tools:::Rd_expr_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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ecoinf.2017.04.008")}.

`akde`

, `raster,UD-method`

```
# 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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