Gorilla Nesting Sites
Locations of nesting sites of gorillas, and associated covariates, in a National Park in Cameroon.
data(gorillas)
gorillas is a marked point pattern (object
of class "ppp") representing nest site locations.
gorillas.extra is a named list of 7 pixel images (objects of
class "im") containing spatial covariates.
It also belongs to the class "listof".
All spatial coordinates are in metres.
The coordinate reference system is WGS_84_UTM_Zone_32N.
These data come from a study of gorillas in the Kagwene Gorilla Sanctuary, Cameroon, by the Wildlife Conservation Society Takamanda-Mone Landscape Project (WCS-TMLP). A detailed description and analysis of the data is reported in Funwi-Gabga and Mateu (2012).
The dataset gorillas is a marked point pattern
(object of class "ppp")
giving the spatial locations of 647 nesting sites of gorilla groups
observed in the sanctuary over time.
Locations are given as UTM (Zone 32N) coordinates in metres.
The observation window is the boundary of the sanctuary, represented
as a polygon. Marks attached to the points are:
Identifier of the gorilla group
that constructed the nest site:
a categorical variable with values major or minor.
Season in which data were collected:
categorical, either rainy or dry.
Day of observation. A value of class "Date".
Note that the data contain duplicated points (two points at the
same location). To determine which points are duplicates,
use duplicated.ppp.
To remove the duplication, use unique.ppp.
The accompanying dataset gorillas.extra contains
spatial covariate information. It is a named list containing
seven pixel images (objects of class "im") giving the values of
seven covariates over the study region. It also belongs
to the class "listof" so that it can be plotted.
The component images are:
Compass direction of the terrain slope.
Categorical, with levels
N,
NE,
E,
SE,
S,
SW,
W and
NW.
Digital elevation of terrain, in metres.
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).
Terrain slope, in degrees.
Type of slope.
Categorical, with values
Valley, Toe (toe slope), Flat,
Midslope, Upper and Ridge.
Vegetation or cover type.
Categorical, with values
Disturbed (highly disturbed forest), Colonising
(colonising forest), Grassland (savannah),
Primary (primary forest), Secondary (secondary forest), and
Transition (transitional vegetation).
Euclidean distance from nearest water body, in metres.
For further information see Funwi-Gabga and Mateu (2012).
For demonstration and training purposes,
the raw data file for the vegetation covariate is
also provided in the spatstat.data package installation,
as the file vegetation.asc in the folder rawdata/gorillas.
Use system.file to obtain the file path:
system.file("rawdata/gorillas/vegetation.asc", package="spatstat.data").
This is a text file in the simple ASCII file format of the geospatial
library GDAL. The file can be read by the function
readGDAL in the rgdal package, or alternatively
read directly using scan.
Field data collector: Wildlife Conservation Society Takamanda-Mone Landscape Project (WCS-TMLP). Please acknowledge WCS-TMLP in any use of these data.
Data kindly provided by Funwi-Gabga Neba, Data Coordinator of A.P.E.S. Database Project, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
The collaboration of Prof Jorge Mateu, Universitat Jaume I, Castellon, Spain is gratefully acknowledged.
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.
if(require(spatstat.geom)) {
summary(gorillas)
plot(gorillas)
plot(gorillas.extra)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.