A lacework design comprises a square grid with detectors placed at regular distances along the grid lines (Efford unpubl.). This requires fewer detectors than uniform coverage at close spacing and is simpler than clustered designs, while providing good spatial coverage and protection from alignment bias (Efford 2019).
make.lacework(region, spacing = c(100, 20), times = NULL, origin = NULL,
rotate = 0, radius = NULL, detector = "multi", keep.design = TRUE)
An secr traps object. The attribute `crossings' is a 2-column matrix with the coordinates of the intersection points. If keep.design is TRUE then the input argument values are retained in attribute `design' (a list with first component function = 'make.lacework').
dataframe or SpatialPolygonsDataFrame with coordinates of perimeter
numeric 2-vector with major (grid) and minor spacings, or minor spacing only
numeric ratio major:minor spacing if spacing length 1
numeric vector giving x- and y-cooordinates of fixed grid origin (origin is otherwise random)
numeric; number of degrees by which to rotate design clockwise about centroid of region bounding box
numeric; detectors are dropped if they are further than this from a crossing
character detector type -- see make.grid
logical; if TRUE then input argument values are retained
It is tidy for the major spacing (spacing[1]) to be a multiple of the minor spacing (spacing[2]); precisely one detector is then placed at each grid intersection. This outcome may also be achieved by providing only the minor spacing in the spacing argument and specifying an integer value for times.
In general it is better not to specify origin. Specifying both origin and rotate may result in incomplete coverage, as the desired grid is relative to the bounding box of the rotated region.
Set radius < spacing[1]/2 to break lacework into multiple cross-shaped arrays centred on the intersections (crossing points) and truncated at radius metres (assuming you follow advice and express all linear measurements in metres).
The number of detectors should not exceed 5000.
Efford, M. G. (2019) Non-circular home ranges and the estimation of population density. Ecology 100, e02580. https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecy.2580
make.systematic
trps <- make.lacework(possumarea, c(1000,100), rotate = 45, detector = 'proximity')
plot(trps, gridspace = 1000)
lines(possumarea)
points(attr(trps, 'crossings'), pch = 16)
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