gorillas_sf: Gorilla nesting sites in sf format

In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

Gorilla nesting sites in sf format

Description

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).

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.

Source

Library spatstat.data.

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() &&
  require(ggplot2, quietly = TRUE) &&
  requireNamespace("terra", quietly = TRUE) &&
  requireNamespace("tidyterra", 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$gcov <- gorillas_sf_gcov()
  ggplot() +
    tidyterra::geom_spatraster(data = gorillas_sf$gcov$elevation)

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

inlabru documentation built on Aug. 9, 2025, 1:08 a.m.