encounter {ctmm}

Encounter statistics

Description

Functions to calculate encounter probabilities and the conditional location distribution of where encounters take place (conditional on said encounters taking place), as described in Noonan et al (2021).

Usage

encounter(object,debias=FALSE,level=0.95,normalize=FALSE,self=TRUE,...)

cde(object,include=NULL,exclude=NULL,debias=FALSE,...) 

Arguments

object

A list of aligned UD objects.

debias

Approximate bias corrections [IN DEVELOPMENT].

level

Confidence level for relative encounter rates.

normalize

Normalize relative encounter rates by the average uncorrelated self-encounter rate.

self

Fix the self-interaction rate appropriately.

include

A matrix of interactions to include in the calculation (see Details below).

exclude

A matrix of interactions to exclude in the calculation (see Details below).

...

Additional arguments for future use.

Details

Encounter probabilities are standardized to 1 meter, and must be multiplied by the square encounter radius (in meters), to obtain other values. If normalize=FALSE, the relative encounter rates have units of 1/m^2 and tend to be very small numbers for very large home-range areas. If normalize=TRUE, the relative encounter rates are normalized by the average uncorrelated self-encounter rate, which is an arbitrary value that provides a convenient scaling.

The include argument is a matrix that indicates which interactions are considered in the calculation. By default, include = 1 - diag(length(object)), which implies that all interactions are considered aside from self-interactions. Alternatively, exclude = 1 - include can be specified, and is by-default exclude = diag(length(object)), which implies that only self-encounters are excluded.

Value

encounter produces an array of standardized encounter probabilities with CIs, while cde produces a single UD object.

Note

Prior to v1.2.0, encounter() calculated the CDE and rates() calculated relative encounter probabilities.

Author(s)

C. H. Fleming

References

M. J. Noonan, R. Martinez-Garcia, G. H. Davis, M. C. Crofoot, R. Kays, B. T. Hirsch, D. Caillaud, E. Payne, A. Sih, D. L. Sinn, O. Spiegel, W. F. Fagan, C. H. Fleming, J. M. Calabrese, “Estimating encounter location distributions from animal tracking data”, Methods in Ecology and Evolution (2021) doi:10.1111/2041-210X.13597.

See Also

akde, overlap

Examples

# Load package and data
library(ctmm)
data(buffalo)

# fit models for first two buffalo
GUESS <- lapply(buffalo[1:2], function(b) ctmm.guess(b,interactive=FALSE) )
# in general, you should use ctmm.select here
FITS <- lapply(1:2, function(i) ctmm.fit(buffalo[[i]],GUESS[[i]]) )
names(FITS) <- names(buffalo[1:2])

# create aligned UDs
UDS <- akde(buffalo[1:2],FITS)

# calculate 100-meter encounter probabilities
P <- encounter(UDS)
P$CI * 100^2

# calculate CDE
CDE <- cde(UDS)

# plot data and encounter distribution
plot(buffalo[1:2],col=c('red','blue'),UD=CDE,col.DF='purple',col.level='purple',col.grid=NA)