Mask Object

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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 ~cover.

In relation to the first purpose, the definition of ‘negligible’ is fluid. Any probability less than 0.001 seems OK in the sense of not causing noticeable bias in density estimates, but this depends on the shape of the detection function (fat-tailed functions such as 'hazard rate' are problematic). New tools for evaluating masks appeared in secr 1.5 (mask.check, esa.plot), and suggest.buffer automates selection of a buffer width.

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

Attribute Description
type `traprect', `trapbuffer', `pdot', `polygon', `clusterrect', `clusterbuffer' (see make.mask) or `user'
polygon vertices of polygon defining habitat boundary, for type = `polygon'
pdotmin threshold of p.(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.

A virtual S4 class ‘mask’ is defined to allow the definition of a method for the generic function raster from the raster package.

Note

A habitat mask is needed by secr.fit, but one will be generated automatically if none is provided. You should be aware of this and check that the default settings (e.g. buffer) are appropriate.

See Also

make.mask, read.mask, mask.check, esa.plot, suggest.buffer, secr.fit, secr density models

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