addTelemetry: Combine Telemetry and Detection Data

Description

Animal locations determined by radiotelemetry can be used to augment capture--recapture data. The procedure in secr is first to form a capthist object containing the telemetry data and then to combine this with true capture--recapture data (e.g. detections from hair-snag DNA) in another capthist object. secr.fit automatically detects the telemetry data in the new object.

Usage

addTelemetry (detectionCH, telemetryCH)

Arguments

detectionCH

single-session capthist object, detector type `proximity' or `count'

telemetryCH

single-session capthist object, detector type `telemetry'

Value

A single-session capthist object with the same detector type as detectionCH, but possibly with empty rows and an `xylist' attribute.

Details

It is assumed that a number of animals have been radiotagged in the vicinity of the detector array, and their telemetry data (xy-coordinates) have been input to telemetryCH, perhaps using read.capthist with detector = "telemetry" and fmt = "XY". A new capthist object is built comprising all the detection histories in detectionCH, plus empty (all-zero) histories for every telemetered animal not in detectionCH. The telemetry locations are carried over from telemetryCH as attribute `xylist' (each component of xylist holds the coordinates of one animal).

Examples

Run this code

# Generate some detection and telemetry data, combine them using
# addTelemetry, and perform analyses

# detectors
te <- make.telemetry()
tr <- make.grid(detector = 'proximity')

# simulated population and 50% telemetry sample
totalpop <- sim.popn(tr, D = 20, buffer = 100)
tepop <- subset(totalpop, runif(nrow(totalpop)) < 0.5)

# simulated detection histories and telemetry
trCH <- sim.capthist(tr,  popn = totalpop, renumber = FALSE)
teCH <- sim.capthist(te, popn = tepop, renumber=FALSE,
    detectpar = list(g0 = 3, sigma = 25))

combinedCH <- addTelemetry(trCH, teCH)

# summarise and display
summary(combinedCH)
plot(combinedCH, border = 150)
ncapt <- apply(combinedCH,1,sum)
points(totalpop[row.names(combinedCH)[ncapt==0],], pch = 1)
points(totalpop[row.names(combinedCH)[ncapt>0],], pch = 16)

fit.tr <- secr.fit(trCH, CL = TRUE)                  ## trapping alone
fit.te <- secr.fit(teCH, CL = TRUE, start = log(20)) ## telemetry alone
fit2   <- secr.fit(combinedCH, CL = TRUE)            ## combined
fit2a   <- secr.fit(combinedCH, CL = TRUE,           ## combined, using info
    details = list(telemetrysigma = TRUE))           ## on sigma from telemetry

# improved precision when focus on realised population
# (compare CVD)
derived(fit.tr, distribution = 'binomial')
derived(fit2, distribution = 'binomial')

# may also use CL = FALSE