Bayesian spatial NBDA for diffusion data with home-base coordinates

Network-based diffusion analysis (NBDA) is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction) from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area) can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN). This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses

Understanding the nesting spatial behaviour of gorillas in the Kagwene Sanctuary, Cameroon

We use spatial point pattern methods to analyse gorilla nest site data, and to enhance our understanding of the nesting behaviour of the Gorilla gorilla diehli in the Kagwene Sanctuary, Cameroon. Data were split into different seasons and different gorilla groups to better understand gorilla nesting behaviour at these different scales. Gorilla nest site distribution was found to be inhomogeneous and clustered, as a result of the inhomogeneity in the distribution of the environmental factors (such as elevation, slope, vegetation and aspect), and because of the interaction between nest sites. The proposed models reflected therefore a combination of the effect of environmental factors and interaction between nest sites. Predictions from these models showed that there is less space available for gorilla nest site location in the dry season than in the rainy season. It also showed that the Minor gorilla group has a bigger niche than the Major group, suggesting a nesting disadvantage in the larger size group. We also found that nest site locations of Major gorilla groups attract Minor groups, and vice versa