encounter | R Documentation |
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).
encounter(object,debias=FALSE,level=0.95,normalize=FALSE,self=TRUE,...)
cde(object,include=NULL,exclude=NULL,debias=FALSE,...)
object |
A |
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. |
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.
encounter
produces an array of standardized encounter probabilities with CIs, while cde
produces a single UD
object.
Prior to v1.2.0, encounter()
calculated the CDE and rates()
calculated relative encounter probabilities.
C. H. Fleming
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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.13597")}.
akde
, overlap
# 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)
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