secr (version 2.9.0)

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)
xy2CH (CH, inflation = 1e-08)

Arguments

detectionCH

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

telemetryCH

single-session capthist object, detector type `telemetry'

CH

single-session capthist object with xylist attribute

inflation

numeric tolerance for polygon

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", or with read.telemetry. 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; use telemetryxy to extract). xy2CH partly reverses addTelemetry: the location information in the xylist attribute is converted back to a capthist with detector type `telemetry'. A search polygon is formed from the convex hull (minimum convex polygon) of the detectors, slightly inflated (factor inflation) to avoid numeric inclusion errors at the vertices.

See Also

capthist, make.telemetry, read.telemetry, telemetryxy

Examples

# 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
# the original animalID (renumber = FALSE) are needed for matching
trCH <- sim.capthist(tr,  popn = totalpop, renumber = FALSE, detectfn = "HHN")
teCH <- sim.capthist(te, popn = tepop, renumber=FALSE, detectfn = "HHN",
    detectpar = list(lambda0 = 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, detectfn = "HHN")  ## trapping alone
fit.te <- secr.fit(teCH, CL = TRUE, start = log(20),   ## telemetry alone
    detectfn = "HHN") 
fit2   <- secr.fit(combinedCH, CL = TRUE,              ## combined
    detectfn = "HHN")                                 
fit2a   <- secr.fit(combinedCH, CL = TRUE, details =   ## combined, using info
    list(telemetrysigma = TRUE), detectfn = "HHN")     ## 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
secr.fit(combinedCH, CL = FALSE, detectfn = "HHN")