secr (version 2.9.4)

housemouse: 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

source

File `examples' distributed with program CAPTURE.

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 (1978 p. 62, 68) cite Coulombe's unpublished 1965 master's thesis from the University of California, Los Angeles, California.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. ll{ 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 }

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
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)