Spatial Capture History Object


A capthist object encapsulates all data needed by, except for the optional habitat mask.


An object of class capthist holds spatial capture histories, detector (trap) locations, individual covariates and other data needed for a spatially explicit capture-recapture analysis with

For ‘single’ and ‘multi’ detectors, capthist is a matrix with one row per animal and one column per occasion (i.e. dim(capthist) = c(nc, noccasions)); each element is either zero (no detection) or a detector number. For other detectors (‘proximity’, ‘count’, ‘signal’ etc.), capthist is an array of values and dim(capthist) = c(nc, noccasions, ntraps); values maybe binary ({–1, 0, 1}) or integer depending on the detector type.

Deaths during the experiment are represented as negative values.

Ancillary data are retained as attributes of a capthist object as follows:

  • traps – object of class traps (required)

  • session – session identifier (required)

  • covariates – dataframe of individual covariates (optional)

  • cutval – threshold of signal strength for detection (‘signal’ only)

  • signalframe – signal strength values etc., one row per detection (‘signal’ only)

  • detectedXY – dataframe of coordinates for location within polygon (‘polygon’-like detectors only)

  • xylist – coordinates of telemetered animals

  • Tu – detectors x occasions matrix of sightings of unmarked animals

  • Tm – detectors x occasions matrix of sightings of marked but unidentified animals

read.capthist is adequate for most data input. Alternatively, the parts of a capthist object can be assembled with the function make.capthist. Use sim.capthist for Monte Carlo simulation (simple models only). Methods are provided to display and manipulate capthist objects (print, summary, plot, rbind, subset, reduce) and to extract and replace attributes (covariates, traps, xy).

A multi-session capthist object is a list in which each component is a capthist for a single session. The list maybe derived directly from multi-session input in Density format, or by combining existing capthist objects with MS.capthist.


Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.

Efford, M. G., Borchers D. L. and Byrom, A. E. (2009) Density estimation by spatially explicit capture-recapture: likelihood-based methods. In: D. L. Thomson, E. G. Cooch and M. J. Conroy (eds) Modeling Demographic Processes in Marked Populations. Springer, New York. Pp. 255–269.

See Also

traps,, read.capthist, make.capthist, sim.capthist, subset.capthist, rbind.capthist, MS.capthist, reduce.capthist, mask

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