mask                  package:secr                  R Documentation

_M_a_s_k _O_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Encapsulate a habitat mask for spatially explicit
     capture-recapture.

_D_e_t_a_i_l_s:

     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 0.01 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 -

       type         'traprect', 'trapbuffer', 'pdot', 'polygon' (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'.

_N_o_t_e:

     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.

_A_u_t_h_o_r(_s):

     Murray Efford murray.efford@otago.ac.nz

_S_e_e _A_l_s_o:

     'make.mask', 'read.mask', 'secr.fit', 'secr density models'

