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.
single-session capthist object, detector type ‘proximity’ or ‘count’
single-session capthist object, detector type ‘telemetry’
single-session capthist object with xylist attribute
numeric tolerance for polygon
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
detector = "telemetry" and
"XY", or with
A new capthist object is built comprising all the detection
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
inflation) to avoid numeric inclusion errors at the vertices.
A single-session capthist object with the same detector type as
detectionCH, but possibly with empty rows and an ‘xylist’ attribute.
Telemetry provides independent data on the location and presence of a sample of animals. These animals may be missed in the main sampling that gives rise to detectionCH i.e., they may have all-zero detection histories.
The ‘telemetry’ detector type is like a ‘polygon’ detector (detections have x-y coordinates). Although perimeter coordinates are required they are not at present used in analyses.
Combining telemetry and detection data is not yet fully documented.
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## Not run: # 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", trace = FALSE) ## End(Not run)