secr (version 2.0.0)

possum: Brushtail Possum Trapping Dataset

Description

Data from a trapping study of brushtail possums at Waitarere, North Island, New Zealand.

Usage

data(possum)

source

Landcare Research, New Zealand.

Details

Brushtail possums (Trichosurus vulpecula) are an unwanted invasive species in New Zealand. Although most abundant in forests, where they occasionally exceed densities of 15 / ha, possums live wherever there are palatable food plants and shelter. Efford et al. (2005) reported a live-trapping study of possums in Pinus radiata plantation on coastal sand dunes. The 294-ha site at Waitarere in the North Island of New Zealand was a peninsula, bounded on one side by the sea and on two other sides by the Manawatu river. Cage traps were set in groups of 36 at 20-m spacing around the perimeter of five squares, each 180 m on a side. The squares ('hollow grids') were centred at random points within the 294-ha area. Animals were tagged and released daily for 5 days in April 2002. Subsequently, leg-hold trapping was conducted on a trapping web centred on each square (data not reported here), and strenuous efforts were made to remove all possums by cyanide poisoning and further leghold trapping across the entire area. This yielded a density estimate of 2.26 possums / ha. Traps could catch at most one animal per day. The live-trapped animals comprised 46 adult females, 33 adult males, 10 immature females and 11 immature males; sex and/or age were not recorded for 4 individuals (M. Coleman unpubl. data). One female possum was twice captured at two sites on one day, having entered a second trap after being released; one record in each pair was selected arbitrarily and discarded. The data are provided as a single-session capthist object 'possumCH'. 'possummask' is a matching mask object - see Examples. Two fitted models ('possum.model.1' & 'possum.model.1b') are provided for illustration. From secr 1.5 a dataframe possumarea is included to indicate the boundary of potential habitat, and this is used to clip possummask. possumarea comprises a single polygon representing the extent of terrestrial vegetation to the west, north and east, and an arbitrary straight southern boundary. The boundary is also included as a shapefile and as a text file (`possumarea.shp' etc. and `possumarea.txt' in the package `extdata' folder). See Examples in make.mask. Clipping of the mask, introduced in secr 1.5, slightly changes the fitted models (and hence the density estimates). ll{ Object Description possumCH capthist object possummask mask object possumarea habitat perimeter possum.model.1 fitted secr model -- null possum.model.1b fitted secr model -- trap response g0 }

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., 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. Efford, M. G., Warburton, B., Coleman, M. C. and Barker, R. J. (2005) A field test of two methods for density estimation. Wildlife Society Bulletin 33, 731--738.

See Also

capthist

Examples

## code used to construct dataset
setwd('d:\density secr 2.0\package data\possum')
possumCH <- read.capthist('foxton.txt', 'foxtraps.txt', detector = 'single')
## drop within-day duplicates of tag 5861 in traps 11,12, to conform DENSITY
possumCH['5861',] <- c(134, 133, 134, 133, 135)
trps <- traps(possumCH)
covariates(traps(possumCH)) <- data.frame(grid =
    factor(substring(rownames(trps),1,2)))
possumarea <- read.table ('possumarea.txt', header = TRUE)
possummask <- make.mask(trps, buffer = 300, type='pdot', pdotmin =
    0.001, detectpar = list(g0=0.2, sigma=60), spacing = 10, poly =
    possumarea)
possum.model.1 <- secr.fit(possumCH, mask = possummask, trace=F)
possum.model.1b <- secr.fit(possumCH, mask = possummask, model =
    list(g0~b), trace=F)
save(possumCH, possummask, possumarea, possum.model.1, possum.model.1b,
    file='d:\density secr 2.0\secr\data\possum.RData')

plot(possummask)
plot(possumCH, tracks = TRUE, add = TRUE)
plot(traps(possumCH), add = TRUE)
lines(possumarea)

summary(possumCH)

## compare & average pre-fitted models
AIC(possum.model.1, possum.model.1b)
model.average(possum.model.1, possum.model.1b)