simPPe: Simulate a spatial point pattern in a heterogeneous landscape

In AHMbook: Functions and Data for the Book 'Applied Hierarchical Modeling in Ecology' Vols 1 and 2

Simulate a spatial point pattern in a heterogeneous landscape

Description

The function simulates a spatial point pattern in a heterogeneous landscape simulated on a square landscape. The study area ('core') is simulated inside the larger landscape that includes a buffer. The size of the core is defined by the lscape.size minus twice the buffer.

There is one habitat covariate X that affects the intensity of the points. X is spatially structured with negative-exponential spatial autocorrelation; the parameters of the field can be chosen to create large 'islands' of similar values or no 'islands' at all, in which case the field is spatially unstructured.

The intensity of STATIC points (e.g. animal activity centers) may be inhomogeneous and affected by the coefficient beta, which is the log-linear effect of X.

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.

Previous versions used RandomFields, but that is not currently available on CRAN. fields is now used, but it cannot deal with large values of lscape.size and theta.X. If you have RandomFields installed (perhaps by getting it from the CRAN archive), you can load a version of simPPe that supports it with source(system.file("RandomFieldsSupport", "simPPe.R", package="AHMbook")).

Usage

simPPe(lscape.size = 150, buffer.width = 25, variance.X = 1, theta.X = 10,
  M = 250, beta = 1, quads.along.side = 6, show.plots = TRUE)

Arguments

lscape.size	size (width = height) of the square landscape, including core and buffer.
buffer.width	width of buffer around core study area.
variance.X	variance of Gaussian random field (covariate X).
theta.X	scale parameter of correlation in field (must be >0).
M	expected number of activity centers in core area.
beta	coefficient of the habitat covariate.
quads.along.side	number of quadrats along the side of the core area; the total number of quadrats will be quads.along.side^2, thus indirectly defining the quadrat area.
show.plots	if TRUE, summary plots are displayed.

Value

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

core	range of x and y coordinates in the 'core'
M2	number of ACs in the total landscape, including the buffer
grid	coordinates of the center of each pixel
pixel.size	length of side of each pixel
size.core	the width=height of the core area
prop.core	the proportion of the landscape inside the core
X	matrix of covariate values for each pixel
probs	matrix of probabilities of an AC being inside each pixel (sums to 1)
pixel.id	the ID of the pixel for each AC
u	2-column matrix, coordinate of each AC
nsite	number of quadrats
quad.size	width = height of each quadrat
breaks	boundaries of the quadrats
mid.pt	mid-points of the quadrats
lambda_pp	intensity of point pattern (ACs per unit area)
Nac	site-specific abundance of ACs
zac	site-specific occurrence (0/1) of ACs
E_N	average realized abundance per quadrat
E_z	average realized occupancy per quadrat

Author(s)

Marc Kéry & Andy Royle.

References

Kéry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 10.

Examples

# Nice plot (produces the really nice Fig. 10-2 in the book)
# RNGkind(sample.kind = "Rounding") # run this for R >= 3.6.0
set.seed(117, kind="Mersenne-Twister")
# Fails if RandomFields is not available
#try(str(dat <- simPPe(lscape.size = 200, buffer.width = 25, variance.X = 1,
#  theta.X = 70, M = 200, beta = 1, quads.along.side = 6)))

str(dat <- simPPe(lscape.size = 200, buffer.width = 20, variance.X = 1,
  theta.X = 5, M = 250, beta = 1, quads.along.side = 6))

AHMbook documentation built on Sept. 12, 2024, 6:37 a.m.