habitat_kernel: Simulate UD from fitted SSF

Description

Function to obtain a habitat kernel from a fitted (i)SSF.

Usage

habitat_kernel(coef, resources, exp = TRUE)
movement_kernel(scale, shape, template, quant = 0.99)
simulate_ud(movement_kernel, habitat_kernel, start, n = 100000L)
simulate_tud(movement_kernel, habitat_kernel, start, n = 100, n_rep = 5000)

Value

A RasterLayer.

Arguments

coef: [list]
Vector with coeffiecients, not yet implemented.
resources: [RasterLayer, RasterStack]
The resources.
exp: A logical scalar, indicating whether or not the resulting habitat kernel should be exponentiated. This is usually TRUE.
scale, shape: [numeric](1)
Scale and scale parameter of the gamma distribution of step lengths.
template: [RasterLayer,RasterStack]
A raster serving as template for the simulations.
quant: A numeric scalar, quantile of the step-length distribution that is the maximum movement distance.
movement_kernel: [RasterLayer]
The movement kernel.
habitat_kernel: [RasterLayer]
The habitat kernel.
start: [numeric(2)]
Starting point of the simulation.
n: [integer(1)=1e5]
The number of simulation steps.
n_rep: [integer(1)=5e3]{>0}
The number of times the animal walks of the final position. The mean of all replicates is returned.

Author

Johannes Signer (jmsigner@gmail.com)

Details

movement_kernel(): calculates a movement kernel from a fitted (i)SSF. The method is currently only implemented for the gamma distribution.

The habitat kernel is calculated by multiplying resources with their corresponding coefficients from the fitted (i)SSF.

simulate_ud(): simulates a utilization distribution (UD) from a fitted Step-Selection Function.

simulate_tud(): Is a convenience wrapper around simulate_ud to simulate transition UDs (i.e., starting at the same position many times and only simulate for a short time).

References

avgar2016amt signer2017amt