expected.d {openCR}

Expected Distance Moved

Description

Movement models in openCR differ in their parameterisation so direct comparison can be difficult. The expected distance moved is a convenient statistic common to all models. This function computes the expected distance from various inputs, including fitted models.

Usage

expected.d(movementmodel, move.a, move.b, truncate = Inf, mask = NULL, 
    min.d = 1e-4, ...)

Arguments

movementmodel

character or function or kernel or openCR object

move.a

numeric parameter of kernel

move.b

numeric parameter of kernel

truncate

radius of truncation

mask

habitat mask object

min.d

numeric lower bound of integration (see Details)

...

other arguments passed to make.kernel if input is a fitted model

Details

The input movementmodel may be

• fitted openCR model

• user kernel function g(r)

• kernel object

• character name of kernel model see Movement models

If truncate (R) is finite or movementmodel is a function then the expected value is computed by numerical integration E(d) = \int_0^R r.f(r) dr. In the event that f(0) is not finite, min.d is used as the lower bound.

mask is used only for ‘uncorrelated’ and ‘uncorrelatedzi’ movement. For these models the expected movement is merely the average distance between points on the mask, weighted by (1-zi) if zero-inflated (uncorrelatedzi).

The ... argument is useful for (i) selecting a session from a fitted model, or (ii) specifying the upper or lower confidence limits from a single-parameter fitted model via the ‘stat’ argument of make.kernel.

Value

A numeric value (zero for 'static' model, NA if model unrecognised).

References

Efford, M. G. and Schofield, M. R. (2022) A review of movement models in open population capture–recapture. Methods in Ecology and Evolution 13, 2106–2118. https://doi.org/10.1111/2041-210X.13947

See Also

Movement models, make.kernel, pkernel, qkernel

Examples

expected.d('BVT', move.a = 20, move.b = 1)
expected.d('BVT', move.a = 20, move.b = 1, truncate = 300)

k <- make.kernel(movementmodel = 'BVT', spacing = 10, move.a = 20, move.b = 1)
expected.d(k)