hornedlizard {secr} | R Documentation

## Flat-tailed Horned Lizard Dataset

### Description

Data from multiple searches for flat-tailed horned lizards (Phrynosoma mcalli) on a plot in Arizona, USA.

### Usage

    hornedlizard

### Details

The flat-tailed horned lizard (Phrynosoma mcalli) is a desert lizard found in parts of southwestern Arizona, southeastern California and northern Mexico. There is considerable concern about its conservation status. The species is cryptically coloured and has the habit of burying under the sand when approached, making it difficult or impossible to obtain a complete count (Grant and Doherty 2007).

K. V. Young conducted a capture–recapture survey of flat-tailed horned lizards 25 km south of Yuma, Arizona, in the Sonoran Desert. The habitat was loose sand dominated by creosote bush and occasional bur-sage and Galletta grass. A 9-ha plot was surveyed 14 times over 17 days (14 June to 1 July 2005). On each occasion the entire 300 m x 300 m plot was searched for lizards. Locations within the plot were recorded by handheld GPS. Lizards were captured by hand and marked individually on their underside with a permanent marker. Marks are lost when the lizard sheds, but this happens infrequently and probably caused few or no identification errors during the 2.5-week study.

A total of 68 individuals were captured 134 times. Exactly half of the individuals were recaptured at least once.

Royle and Young (2008) analysed the present dataset to demonstrate a method for density estimation using data augmentation and MCMC simulation. They noted that the plot size was much larger than has been suggested as being practical in operational monitoring efforts for this species, that the plot was chosen specifically because a high density of individuals was present, and that high densities typically correspond to less movement in this species. The state space in their analysis was a square comprising the searched area and a 100-m buffer (J. A. Royle pers. comm.).

The detector type for these data is ‘polygonX’ and there is a single detector (the square plot). The data comprise a capture history matrix (the body of `hornedlizardCH`) and the x-y coordinates of each positive detection (stored as an attribute that may be displayed with the ‘xy’ function); the ‘traps’ attribute of `hornedlizardCH` contains the vertices of the plot. See secr-datainput.pdf for guidance on data input.

Non-zero entries in a polygonX capture-history matrix indicate the number of the polygon containing the detection. In this case there was just one polygon, so entries are 0 or 1. No animal can appear more than once per occasion with the polygonX detector type, so there is no need to specify ‘binomN = 1’ in secr.fit.

Object | Description
hornedlizardCH | single-session capthist object

### Source

Royle and Young (2008) and J. A. Royle (pers. comm.), with additional information from K. V. Young (pers. comm.).

### References

Efford, M. G. (2011) Estimation of population density by spatially explicit capture–recapture analysis of data from area searches. Ecology 92, 2202–2207.

Grant, T. J. and Doherty, P. F. (2007) Monitoring of the flat-tailed horned lizard with methods incorporating detection probability. Journal of Wildlife Management 71, 1050–1056

Marques, T. A., Thomas, L. and Royle, J. A. (2011) A hierarchical model for spatial capture–recapture data: Comment. Ecology 92, 526–528.

Royle, J. A. and Young, K. V. (2008) A hierarchical model for spatial capture–recapture data. Ecology 89, 2281–2289.

### See Also

`capthist`, `detector`, `reduce.capthist`

### Examples

    plot(hornedlizardCH, tracks = TRUE, varycol = FALSE,
        lab1 = TRUE, laboff = 6, border = 10, title =
        "Flat-tailed Horned Lizards (Royle & Young 2008)")

    table(table(animalID(hornedlizardCH)))
    traps(hornedlizardCH)

    ## show first few x-y coordinates
    head(xy(hornedlizardCH))

    ## Not run:
    ## Compare default (Poisson) and binomial models for number
    ## caught
    FTHL.fit <- secr.fit(hornedlizardCH)
    FTHLbn.fit <- secr.fit(hornedlizardCH, details =
        list(distribution = "binomial"))
    collate(FTHL.fit, FTHLbn.fit)[,,,"D"]

    ## Collapse occasions (does not run faster)
    hornedlizardCH.14 <- reduce(hornedlizardCH, newoccasions =
        list(1:14), outputdetector = "polygon")
    FTHL14.fit <- secr.fit(hornedlizardCH.14, binomN = 14)

    ## End(Not run)

[Package secr version 3.2.1 Index]