title: Bayesian Spatial NBDA for Diffusion Data with Home-Base Coordinates.
abstract: 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.
authors.affiliation: School of Geography and Geosciences, University of St. Andrews, St. Andrews, Scotland, United Kingdom.
authors.forename: Glenna F
authors.initials: GF
authors.lastname: Nightingale
authors.affiliation: School of Biology, University of St. Andrews, St. Andrews, Scotland, United Kingdom.
authors.forename: Kevin N
authors.initials: KN
authors.lastname: Laland
authors.affiliation: School of Life Sciences, Anglia Ruskin University, Cambridge, England, United Kingdom.
authors.forename: William
authors.initials: W
authors.lastname: Hoppitt
authors.affiliation: School of Computer Science, University of St. Andrews, St. Andrews, Scotland, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Nightingale
mesh_terms: D063147:Animal Distribution / Q000502:physiology*; D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D066264:Datasets as Topic; D006801:Humans; D015233:Models, Statistical; D063809:Satellite Imagery / Q000706:statistics & numerical data*