Description
Encapsulate a habitat mask for spatially explicit capture--recapture.Details
A habitat mask serves four main purposes in spatially explicit
capture--recapture. Firstly, it defines an outer limit to the area of
integration; habitat beyond the mask may be occupied, but animals there
should have negligible chance of being detected (see pdot
and below). Secondly, it distinguishes sites in the vicinity of the
detector array that are 'habitat' (i.e. have the potential to be
occupied) from 'non-habitat'. Thirdly, it discretizes continuous habitat
as a list of points. Each point is notionally associated with a cell
(pixel) of uniform density. Discretization allows the SECR likelihood to
be evaluated by summing over grid cells. Fourthly, the x-y coordinates
of the mask and any habitat covariates may be used to build spatial
models of density. For example, a continuous or categorical habitat
covariate 'cover' measured at each point on the mask might be used in a
formula for density such as D = $\sim$cover.
In relation to the first purpose, the definition of 'negligible' is
fluid. Any probability less than 0.01 seems OK in the sense of not
causing noticeable bias in density estimates, but extent of the mask
affects the binomial sampling variance of density derived from
conditional likelihood estimates of the detection function (M. Efford
unpubl. results).
Mask points are stored in a data frame with columns 'x' and 'y'. The
number of rows equals the number of points.
Possible mask attributes --
ll{
type 'traprect', 'trapbuffer', 'pdot', 'polygon' (see make.mask) or 'user'
polygon vertices of polygon defining habitat boundary, for type = 'polygon'
pdotmin threshold of $p.(\mathbf{X})$ for type = 'pdot'
covariates dataframe of site-specific covariates
meanSD data frame with centroid (mean and SD) of x and y coordinates
area area (ha) of the grid cell associated with each point
spacing nominal spacing (metres) between adjacent points
boundingbox data frame of 4 rows, the vertices of the bounding box of all grid cells in the mask
}
Attributes other than covariates are generated automatically by make.mask. Type 'user' refers to masks input from a text file with read.mask.