Rescale trap coordinates, individual-level grid extent, and starting activity center coordinates for local SCR approach

Rescale inputs to prepare data for habitat mask to be used.

rescale_local(X, ext, ext_mat, s.st, site, hab_mask)

Arguments

X

An array representing the coordinates of traps in UTMs for each individual. This is returned from localize_classic.

ext

An Extent object from the raster package. This is returned from localize_classic.

ext_mat

A matrix of individual state-space grid extents returned from localize_classic.

s.st

A matrix of starting activity center coordinates. This is returned from localize_classic

site

Either NULL (if a 2D trap array is used) or a vector of integers denoting which trap array an individual (either detected or augmented) belongs to. Note that site is provided from sim_classic when a 3D trap array is used. However, this site variable must be correctly augmented based on the total augmented population size (i.e., M).

hab_mask

A matrix or arary output from mask_polygon or mask_raster functions.

Value

  • X Rescaled trap coordinates

  • ext_mat Rescaled matrix of individual state-space grid extents.

  • s.st A matrix of rescaled starting activity center coordinates.

Details

This function is only meant to be used when habitat masking is incorporated into a local SCR model. The functions properly rescales inputs for this SCR approach based onthe habitat mask. Note that the pixelWidth needs to be included as an input in the model after inputs are rescaled to correctly estimate the scaling parameter (i.e., 'sigma').

Author

Daniel Eacker

Examples

if (FALSE) {
# simulate a single trap array with random positional noise
x <- seq(-1600, 1600, length.out = 6)
y <- seq(-1600, 1600, length.out = 6)
traps <- as.matrix(expand.grid(x = x, y = y))
# add some random noise to locations
set.seed(100)
traps <- traps + runif(prod(dim(traps)),-20,20) 
mysigma = 300 # simulate sigma of 300 m
mycrs = 32608 # EPSG for WGS 84 / UTM zone 8N
pixelWidth = 100 # grid resolution

# Simulated abundance
Nsim = 250

# Create initial grid and extent (use a slightly bigger buffer to match
# scaled-up state-space below)
Grid = grid_classic(X = traps, crs_ = mycrs, buff = 3.7*mysigma, res = pixelWidth)

# create polygon to use as a mask
library(sf)
poly = st_sfc(st_polygon(x=list(matrix(c(-2465,-2465,2530,-2550,2650,2550,
0,2550,-800,2500,-2350,2300,-2465,-2465),ncol=2, byrow=TRUE))), crs =  mycrs)

# make simple plot
par(mfrow=c(1,1))
plot(Grid$grid, pch=20)
points(traps, col="blue",pch=20)
plot(poly, add=TRUE)

# create habitat mask from polygon
hab_mask = mask_polygon(poly = poly, grid = Grid$grid, crs_ = mycrs, 
prev_mask = NULL)

data3d = sim_classic(X = traps, ext = Grid$ext, crs_ = mycrs, sigma_ = mysigma, 
                  prop_sex = 1, N = Nsim, K = 4, base_encounter = 0.15, 
                  enc_dist = "binomial", hab_mask = hab_mask, setSeed = 100)

# generate initial activity center coordinates for 2D trap array without 
# habitat mask
s.st = initialize_classic(y=data3d$y, M=500, X=traps, ext = Grid$ext, 
hab_mask = hab_mask)

# now use grid_classic to create an individual-level state-space (with origin 0, 0)
Grid_ind = grid_classic(X = matrix(c(0,0),nrow=1), crs_ = mycrs, 
                        buff = 3*mysigma, res = 100)

# now localize the data components created above 
local_list = localize_classic(y = data3d$y, grid_ind = Grid_ind$grid, X=traps, 
                            crs_ = mycrs, sigma_ = mysigma, s.st = s.st,
                            hab_mask = hab_mask)

# rescale inputs
rescale_list = rescale_local(X = local_list$X, ext = local_list$ext, 
                             ext_mat = local_list$ext_mat,
                             s.st = local_list$s.st, hab_mask = hab_mask)

# inspect rescaled list
str(rescale_list)
}