This simulates line transect distance sampling data with a spatial distribution of objects in a heterogeneous landscape where the parameter beta controls the effect of habitat. Multiple sample occasions are simulated and temporary emigration is allowed (parameter phi). Habitat is simulated according to a Gaussian random field model defined within the function. Uses a half normal detection model (if perp = TRUE) or a Gaussian hazard model (perp = FALSE).
To recreate the data sets used in the book with R 3.6.0 or later, include sample.kind="Rounding"
in the call to set.seed
. This should only be used for reproduction of old results.
simSpatialDSte(nsites=28, dim=10, beta=1, lam0=2.5, nsurveys=4, sigma=3,
phi=0.6, theta=2, show.plots=3)
number of sites
number of pixels along each side of the square site
the effect of habitat on the number of individuals in a pixel.
expected population size at each site
the number of replicate surveys
scale of half-normal detection function
probability an individual is available for detection, ie, not temporarily emigrated.
exponential correlation in the spatial covariate.
the number of sites for which plots should be displayed; set to 0 to suppress plotting.
A list with the values of the input arguments and the following additional elements:
the number of pixels in each site (= dim^2)
distance from line to edge of square (= dim/2)
true number of individuals at each site
perpendicular distance of each pixel from the line
pixels x sites matrix, value of habitat covariate for each pixel
sites x pixels x surveys array, number of animals detected
sites x surveys matrix, number of animals detected (summed over pixels)
K<U+00E9>ry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology.
Mizel, J.D., Schmidt, J.H., & Lindberg, M.S. (2018) Accommodating temporary emigration in spatial distance sampling models. Journal of Applied Ecology, 55, 1456-1464.
K<U+00E9>ry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 11.
# NOT RUN {
# Run the function with default values and look at the output
str(tmp <- simSpatialDSte())
# }
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