costing: Cost of SECR design

Description

The cost of implementing a spatially explicit capture--recapture design depends on the detector layout, the number of detections and the various unit costs.

Usage

costing(traps, nr, noccasions, unitcost = list(), nrepeats = 1, routelength = NULL, 
    setupoccasion = TRUE)

Value

A named numeric vector

Arguments

traps: traps object for detector array
nr: numeric vector with E(\(n\)) and E(\(r\)) as first two elements
noccasions: integer number of sampling occasions
unitcost: list with unit costs (see Details)
nrepeats: integer number of repeated arrays
routelength: numeric route length (km)
setupoccasion: logical; if TRUE then the cost of a setup visit is included (noccasions+1)

Details

nr is a vector with the expected sample sizes (numbers of individuals and recaptures), usually the output from Enrm.

unitcost should be a list with at least one of the components `perkm', `perarray', `perdetector', `pervisit' and `perdetection'.

The number of occasions (noccasions) is incremented by 1 if setupoccasion is TRUE.

Component	Unit cost	Costing	Arrays
perarray	perarray x nrepeats	Detectors	perdetector
perdetector x nrow(traps) x nrepeats	Travel	perkm	perkm x routelength x noccasions x nrepeats
Visits	pervisit	sum(pervisit x trapcost) x noccasions x nrepeats	Detections

`Travel' and `Visits' are alternative ways to cost field time. The variable `routelength' represents the length of a path followed to visit all detectors; if not specified it is approximated by the sum of the nearest-trap distances. The variable `trapcost' is a vector of length equal to the number of detectors. By default it is a vector of 1's, but detector- specific values may be provided as trap covariate `costpervisit'. In the latter case the value of `pervisit' should probably be 1.0.

`Arrays' and `Detectors` represent one-off costs.

`Detections' includes costs such as handling time and laboratory DNA analysis.

See ../doc/secrdesign-tools.pdf for more.

Examples

Run this code


tr <- make.grid(8, 8, spacing = 25)
msk <- make.mask(tr, buffer = 100, type = 'trapbuffer')
nrm <- Enrm(D = 5, tr, msk, list(lambda0 = 0.2, sigma = 20), 5)
costing (tr, nrm, 5, unitcost = list(pervisit = 5, perdetection = 15))