secr (version 1.3.0)

sim.capthist: Simulate Detection Histories

Description

Create a set of capture or marking-and-resighting histories by simulated sampling of a 2-D population using an array of detectors.

Usage

sim.capthist(traps, popn = list(D = 5, buffer = 100,
    Ndist = 'poisson'), detectfn = 0, detectpar = list(), 
    noccasions = 5, renumber = TRUE, seed = NULL)
sim.resight(traps, ..., q = 1, pID = 1, unmarked = TRUE,
    nonID = TRUE)

Arguments

traps

traps object with the locations and other attributes of detectors

popn

locations of individuals in the population to be sampled, either as a popn object or a list with named elements 'D' (density) and 'buffer'

detectfn

code for type of detection function

detectpar

list of values for named parameters of detection function

noccasions

number of occasions to simulate

renumber

logical for whether output rows should labeled sequentially (TRUE) or retain the numbering of the population from which they were drawn (FALSE)

seed

an object specifying if and how the random number generator should be initialized ('seeded')

...

arguments to pass to sim.capthist

q

number of marking occasions

pID

probability of individual identification for marked animals

unmarked

logical, if true unmarked individuals are not recorded during 'sighting'

nonID

logical, if true then unidentified marked individuals are not recorded during 'sighting'

Value

• For sim.capthist, an object of class capthist, a matrix or 3-dimensional array with additional attributes. Rows represent individuals and columns represent occasions; the third dimension, used when detector type = 'proximity', codes presence or absence at each detector. For trap detectors ('single', 'multi') each entry in capthist is either zero (no detection) or the sequence number of the trap. The initial state of the R random number generator is stored in the 'seed' attribute. For sim.resight, an object of class capthist, always a 3-dimensional array, with additional attributes Tu and Tm containing counts of 'unmarked' and 'marked, not identified' sightings.

Details

If popn is not of class 'popn' then a homogeneous Poisson population with the desired density (animals/ha) is first simulated over the rectangular area of the bounding box of traps plus a buffer of the requested width (metres). The detection algorithm depends on the detector type of traps. For 'proximity' detectors, the actual detection probability of animal i at detector j is the naive probability given by the detection function. For 'single' and 'multi' detectors the naive probability is modified by competition between detectors and, in the case of 'single' detectors, between animals. See Efford (2004) and other papers below for details. Detection parameters are specific to the detection function, which is indicated by a numeric code (detection functions). Parameters may vary with time - for this provide a vector of length noccasion. The default detection parameters are list(g0 = 0.2, sigma = 25, z = 1). detectpar may also include 'binomN' and other arguments for detectors that have yet to be documented. A zero value for binomN indicates that counts should be modelled with a Poisson distribution. If popn is specified by an object of class 'popn' then any individual covariates will be passed on; the covariates attribute of the output is otherwise set to NULL. The random number seed is managed as in simulate. sim.resight generates mark-resight data for 'q' marking occasions followed by 'noccasion -- q' sighting occasions. sim.capthist is first called with the arguments 'traps' and .... The detector type must be 'proximity'. The 'usage' attribute of traps is ignored at present, so the same detectors are operated on all occasions. Any detection-parameter vector of length 2 in ...is interpreted as providing differing constant values for the marking and sighting phases.

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.

See Also

sim.popn, capthist, traps, popn, detection functions, simulate

Examples

## simple example
## detector = 'multi' (default)
temptrap <- make.grid(nx = 6, ny = 6, spacing = 20)
sim.capthist (temptrap, detectpar = list(g0 = 0.2, sigma = 20))

## with detector = 'proximity, there may be more than one 
## detection per individual per occasion
temptrap <- make.grid(nx = 6, ny = 6, spacing = 20, detector = 
    'proximity')
summary(sim.capthist (temptrap, detectpar = 
    list(g0 = 0.2, sigma = 20)))