housemouse {secr} | R Documentation

## House mouse live trapping data

### Description

Data of H. N. Coulombe from live trapping of feral house mice (Mus musculus) in a salt marsh, California, USA.

### Usage

    housemouse

### Details

H. N. Coulombe conducted a live-trapping study on an outbreak of feral house mice in a salt marsh in mid-December 1962 at Ballana Creek, Los Angeles County, California. A square 10 x 10 grid was used with 100 Sherman traps spaced 3 m apart. Trapping was done twice daily, morning and evening, for 5 days.

The dataset was described by Otis et al. (1978) and distributed with their CAPTURE software (now available from http://www.mbr-pwrc.usgs.gov/software.html). Otis et al. (1978 p. 62, 68) cite Coulombe's unpublished 1965 master's thesis from the University of California, Los Angeles, California.

The data are provided as a single-session `capthist` object. There are two individual covariates: sex (factor levels ‘f’, ‘m’) and age class (factor levels ‘j’, ‘sa’, ‘a’). The sex of two animals is not available (NA); it is necessary to drop these records for analyses using ‘sex’ unless missing values are specifically allowed, as in `hcov`.

The datasets were originally in the CAPTURE ‘xy complete’ format which for each detection gives the ‘column’ and ‘row’ numbers of the trap (e.g. ‘ 9 5’ for a capture in the trap at position (x=9, y=5) on the grid). Trap identifiers have been recoded as strings with no spaces by inserting zeros (e.g. ‘0905’ in this example).

Sherman traps are designed to capture one animal at a time, but the data include 30 double captures and one occasion when there were 4 individuals in a trap at one time. The true detector type therefore falls between ‘single’ and ‘multi’. Detector type is set to ‘multi’ in the distributed data objects.

Otis et al. (1978) report various analyses including a closure test on the full data, and model selection and density estimation on data from the mornings only. We include several secr models fitted to the ‘morning’ data (`morning.0`, `morning.b` etc.). Of these, a model including individual heterogeneity in both g0 and sigma has the lowest AIC.

Object | Description
housemouse | capthist object
housemouse.0 | fitted secr model -- null
housemouse.ampm | fitted secr model -- g0 differs morning vs afternoon
housemouse.ampmh2h2 | fitted secr model -- as above, finite mixture g0, sigma
morning.0 | fitted secr model -- morning data only, null
morning.0h2 | fitted secr model -- mornings, null g0, finite mixture sigma
morning.b | fitted secr model -- mornings, trap response g0
morning.h2 | fitted secr model -- mornings, finite mixture g0
morning.h2h2 | fitted secr model -- mornings, finite mixture g0, sigma
morning.t | fitted secr model -- mornings, day-specific g0

### Source

File ‘examples’ distributed with program CAPTURE.

### References

Otis, D. L., Burnham, K. P., White, G. C. and Anderson, D. R. (1978) Statistical inference from capture data on closed animal populations. Wildlife Monographs 62, 1–135.

### Examples

    plot(housemouse, title = paste("Coulombe (1965), Mus musculus,",
        "California salt marsh"), border = 5, rad = 0.5,
        gridlines = FALSE)

    morning <- subset(housemouse, occ = c(1,3,5,7,9))
    summary(morning)

    ## drop 2 unknown-sex mice
    known.sex <- subset(housemouse, !is.na(covariates(housemouse)$sex))

    ## reveal multiple captures
    table(trap(housemouse), occasion(housemouse))

    AIC(morning.0, morning.b, morning.t, morning.h2, morning.0h2, morning.h2h2)

    ## assess need to distinguish morning and afternoon samples
    ## Not run:
    housemouse.0 <- secr.fit (housemouse, buffer = 20)
    housemouse.ampm <- secr.fit (housemouse, model = g0~tcov, buffer = 20,
        timecov = c(0,1,0,1,0,1,0,1,0,1))
    AIC(housemouse.0, housemouse.ampm)

    ## End(Not run)

[Package secr version 3.2.1 Index]