Spatially Explicit Capture–Recapture Models

Description

Analyse data from a spatially distributed animal population sampled with an array of passive detectors, such as traps.

Details

Package:	secr
Type:	Package
Version:	1.2.11
Date:	2009-12-22
License:	GNU General Public License Version 2 or later

Warning: Version 1.2.11 should be viewed as a beta release: some functions may not work with all documented settings. Feedback is very welcome, including suggestions for additional documentation or new features consistent with the overall design.

Data comprise the locations of detectors (traps) in an object of class 'traps' and the detection histories of individually marked animals in an object of class 'capthist'. Models for population density and detection are defined using symbolic formula notation. Possible predictors for detection probability include several pre-defined variables (t, b etc.) corresponding to 'time', 'behaviour' and other effects. Habitat is distinguished from nonhabitat with an object of class 'mask'. Models are fitted by maximizing either the full likelihood or the likelihood conditional on the number of individuals (n). Conditional likelihood models, while limited to homogeneous Poisson density, allow continuous individual covariates for detection. Fitting creates an object of class secr. Generic methods (plot, print, summary etc.) are available for each object class.

A more extensive overview can be got by typing

RShowDoc ('secr-overview', package='secr')

at the R prompt after the package has been loaded.

The analyses in secr extend those available in the software Density (see www.otago.ac.nz/density for the most recent version of Density).

Acknowledgements

David Borchers made these methods possible with his work on the likelihood, and I'm grateful for his continuing advice. Jeff Laake provided encouragement and reviewed an early version. Ray Brownrigg got my Windows code running under Unix. Deanna Dawson editted some of the documentation (the cleaner bits!) and her support and collaboration were important throughout.

Author(s)

Murray Efford murray.efford@otago.ac.nz

References

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

Efford, M. G. (2004) Density estimation in live-trapping studies. Oikos 106, 598–610.

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.

Efford, M. G., Dawson, D. K. and Borchers, D. L. (2009) Population density estimated from locations of individuals on a passive detector array. Ecology 90, 2676–2682.

Efford, M. G., Dawson, D. K. and Robbins C. S. (2004) DENSITY: software for analysing capture-recapture data from passive detector arrays. Animal Biodiversity and Conservation 27, 217–228.

See Also

secr.fit, traps, capthist, mask

Examples

## Not run: 

## generate some data & plot
detectors  <- make.grid (nx = 10, ny = 10, spacing = 20, 
    detector = 'multi')
plot(detectors, label = TRUE, border = 0, gridspace = 20)
detections <- sim.capthist (detectors, noccasions = 5,
    popn = list(D = 5, buffer = 100), 
    detectpar = list(g0 = 0.2, sigma = 25))
session(detections) <- 'Simulated data'
plot(detections, border = 20, tracks = TRUE, varycol = TRUE)

## generate habitat mask
mask <- make.mask (detectors, buffer = 100, nx = 48)

## fit model and display results
secr.model <- secr.fit (detections, model = g0~b, mask = mask)
secr.model

## End(Not run)