addTelemetry {secr} | R Documentation

## 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, type = c('concurrent','dependent','independent'),
       collapsetelemetry = TRUE, verify = TRUE)

    xy2CH (CH, inflation = 1e-08)

    telemetrytype (object) <- value

    telemetrytype (object, ...)

### Arguments

`detectionCH` | single-session capthist object, detector type ‘single’, ‘multi’, ‘proximity’ or ‘count’
`telemetryCH` | single-session capthist object, detector type ‘telemetryonly’
`type` | character (see Details)
`collapsetelemetry` | logical; if TRUE then telemetry occasions are collapsed to one
`verify` | logical; if TRUE then `verify.capthist` is called on the output
`CH` | capthist object with telemetryxy attribute
`inflation` | numeric tolerance for polygon
`object` | secr traps object
`value` | character telemetry type replacement value
`...` | other arguments

### Details

It is assumed that a number of animals have been radiotagged, and their telemetry data (xy-coordinates) have been input to `telemetryCH`, perhaps using `read.capthist` with `detector = "telemetryonly"` 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`. Telemetry is associated with new sampling occasions and a new detector (nominally at the same point as the first in `detectionCH`). The number of telemetry fixes of each animal is recorded in the relevant cell of the new capthist object (CH[i, s, K+1] for animal i and occasion s if there were K detectors in detectionCH).

The new sampling occasion(s) are assigned the detector type ‘telemetry’ in the traps attribute of the output capthist object, and the traps attribute `telemetrytype` is set to the value provided. The telemetry type may be “independent” (no matching of individuals in captured and telemetered samples), “dependent” (telemetered animals are a subset of captured animals) or “concurrent” (histories may be capture-only, telemetry-only or both capture and telemetry).

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).

The default behaviour of 'addTelemetry' is to automatically collapse all telemetry occasions into one. This is computationally more efficient than the alternative, but closes off some possible models.

`xy2CH` partly reverses `addTelemetry`: the location information in the telemetryxy attribute is converted back to a capthist with detector type ‘telemetry’.

### Value

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

### Note

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 used for telemetry occasions in a combined dataset.

### See Also

`capthist`, `make.telemetry`, `read.telemetry`, `telemetryxy` `telemetered`

### Examples

    ## 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)

    # for later comparison of precison we must fix the habitat mask
    mask <- make.mask(tr, buffer = 100)
    fit.tr <- secr.fit(trCH, mask = mask, CL = TRUE, detectfn = "HHN")  ## trapping alone
    fit.te <- secr.fit(teCH, mask = mask, CL = TRUE, start = log(20),   ## telemetry alone
        detectfn = "HHN")
    fit2   <- secr.fit(combinedCH, mask = mask, CL = TRUE,              ## combined
        detectfn = "HHN")

    # 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)

[Package secr version 3.2.1 Index]