Gorilla nesting sites in sf format — gorillas

This is the gorillas dataset from the package spatstat.data, reformatted as point process data for use with inlabru.

Usage

gorillas_sf
data(gorillas_sf, package = "inlabru")

gorillas_sf_gcov()

gorillas_sp()

Format

The data are a list that contains these elements:

nests:

An sf object containing the locations of the gorilla nests.

boundary:

An sf object defining the boundary of the region that was searched for the nests.

mesh:

An fm_mesh_2d object containing a mesh that can be used with function lgcp to fit a LGCP to the nest data.

gcov_file:

The in-package filename of a terra::SpatRaster object, with one layer for each of these spatial covariates:

aspect: Compass direction of the terrain slope. Categorical, with levels N, NE, E, SE, S, SW, W and NW, which are coded as integers 1 to 8.
elevation: Digital elevation of terrain, in metres.
heat: Heat Load Index at each point on the surface (Beer's aspect), discretised. Categorical with values Warmest (Beer's aspect between 0 and 0.999), Moderate (Beer's aspect between 1 and 1.999), Coolest (Beer's aspect equals 2). These are coded as integers 1, 2 and 3, in that order.
slopangle: Terrain slope, in degrees.
slopetype: Type of slope. Categorical, with values Valley, Toe (toe slope), Flat, Midslope, Upper and Ridge. These are coded as integers 1 to 6.
vegetation: Vegetation type: a categorical variable with 6 levels coded as integers 1 to 6 (in order of increasing expected habitat suitability)
waterdist: Euclidean distance from nearest water body, in metres.

Loading of the covariates can be done with gorillas_sf_gcov() or

gorillas_sf$gcov <- terra::rast(
  system.file(gorillas_sf$gcov_file, package = "inlabru")
)

plotsample

Plot sample of gorilla nests, sampling 9x9 over the region, with 60\

counts: An sf object with elements count, exposure, and geometry, holding the point geometry for the centre of each plot, the count in each plot and the area of each plot.
plots: An sf object with MULTIPOLYGON objects defining the individual plot boundaries and an all-ones weight column.
nests: An sf giving the locations of each detected nests, group ("minor" or "major"), season ("dry" or "rainy"), and date (in Date format).

Source

Library spatstat.data.

Functions

gorillas_sf_gcov(): Access the gorillas_sf covariates data as a terra::rast() object.
gorillas_sp(): Access the gorillas_sf data in sp format. The covariate data is added as gcov, a list of sp::SpatialPixelsDataFrame objects. Requires the sp, sf, and terra packages to be installed.

References

Funwi-Gabga, N. (2008) A pastoralist survey and fire impact assessment in the Kagwene Gorilla Sanctuary, Cameroon. M.Sc. thesis, Geology and Environmental Science, University of Buea, Cameroon.

Funwi-Gabga, N. and Mateu, J. (2012) Understanding the nesting spatial behaviour of gorillas in the Kagwene Sanctuary, Cameroon. Stochastic Environmental Research and Risk Assessment 26 (6), 793-811.

Examples

if (interactive() &&
  bru_safe_inla() &&
  bru_safe_sp() &&
  require("sp") &&
  require(ggplot2, quietly = TRUE) &&
  requireNamespace("terra", quietly = TRUE)) {
  # plot all the nests, mesh and boundary
  ggplot() +
    gg(gorillas_sf$mesh) +
    geom_sf(
      data = gorillas_sf$boundary,
      alpha = 0.1, fill = "blue"
    ) +
    geom_sf(data = gorillas_sf$nests)

  # Plot the elevation covariate
  gorillas_sf$gcov <- terra::rast(
    system.file(gorillas_sf$gcov_file, package = "inlabru")
  )
  plot(gorillas_sf$gcov$elevation)

  # Plot the plot sample
  ggplot() +
    geom_sf(data = gorillas_sf$plotsample$plots) +
    geom_sf(data = gorillas_sf$plotsample$nests)
}
if (FALSE) { # \dontrun{
if (requireNamespace("terra", quietly = TRUE)) {
  gorillas_sf$gcov <- gorillas_sf_gcov()
}
} # }