simSpatialDSline {AHMbook}

Simulate data for a standardized line transect

Description

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. 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.

Usage

simSpatialDSline(N=1000, beta = 1, sigma=0.25, alpha0 = -2, W=1/2, L = 4,
    perp=FALSE, show.plots=TRUE)

Arguments

N

total population size in the rectangle

beta

coefficient of spatial covariate x for the density model.

sigma

scale of half-normal detection function

alpha0

intercept of the hazard function.

W

half-width of the rectangle, which extends W each side of the transect line.

L

length of the transect.

perp

if TRUE, data are simulated for a traditional distance sampling model with perpendicular distances; if FALSE (the default) a model with 'forward distance' data, ie, the distance from the observer to the animal on first detection.

show.plots

if TRUE, summary plots are displayed.

Value

A list with the values of the input arguments and the following additional elements:

delta

the distance between pixel centers (spatial resolution of the raster

grid

2-column matrix with x/y coordinates of all pixels

Habitat

value of habitat covariate for each pixel

Habraster

a Raster object with the habitat covariate

u1, u2

x and y coordinates for all the animals in the population

traps

2-column matrix of trap locations

If perp = TRUE we have

data

a 2-column matrix with x and y coordinates of each animal captured.

pixel

pixel ID for each animal captured.

and if perp = FALSE we have

data

a matrix with rows for each animal captured and columns for trap of first capture, distance from trap to animal, and x and y coordinates of the animal.

pbar

probability that each animal is the population is captured at least once

pixel

pixel ID for each animal captured.

Author(s)

Marc Kéry & Andy Royle

References

Kéry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 11.

Examples

# Run the function with default values and look at the output
str(tmp <- simSpatialDSline(), 1)  # use str(., max.level=1) to limit the amount of output.