kfn: Overlap Index

In secr: Spatially Explicit Capture-Recapture

Overlap Index

Description

Computes the overlap index of Efford et al. (2016) from various inputs, including fitted models.

Usage

kfn(object)

Arguments

object

fitted secr model, numeric vector, matrix, dataframe

Details

kfn simply computes k = \sigma \sqrt D / 100, where \sigma is the sigma parameter of a fitted halfnormal detection function and D is the corresponding density estimate. The factor of 1/100 adjusts for the units used in secr (sigma in metres; D in animals per hectare).

Input may be in any of these forms

1. vector with D and sigma in the first and second positions.

2. matrix with each row as in (1)

3. dataframe such as produced by predict.secr with rows ‘D’ and ‘sigma’, and column ‘estimate’.

4. fitted secr model

5. a list of any of the above

Value

Numeric vector with elements ‘D’, ‘sigma’ and ‘k’, or a matrix with these columns.

Note

The index should not be taken too literally as a measure of overlap: it represents the overlap expected if activity centres are randomly distributed and if home ranges have bivariate normal utilisation. Thus it does not measure overlap due to social behaviour etc. except as that affects home range size.

The index may be estimated directly using the sigmak parameterization (i.e., when sigmak appears in the model for secr.fit). This provides SE and confidence limits for sigmak (= k). However, the directly estimated value of sigmak lacks the unit correction and is therefore 100 \times the value from kfn.

References

Efford, M. G., Dawson, D. K., Jhala, Y. V. and Qureshi, Q. (2016) Density-dependent home-range size revealed by spatially explicit capture–recapture. Ecography 39, 676–688.

See Also

predict.secr, secr.fit, details

Examples

kfn(secrdemo.0)

## compare
## fitk <- secr.fit(captdata, model = sigmak~1, buffer = 100, trace = FALSE)
## predict(fitk)

secr documentation built on Oct. 18, 2023, 1:07 a.m.