Extracts either either a random point from a home range vertice (i.e. a SpatialPolygonsDataFrame object) or a probability-weighted random point from a Utilisation Distribution (i.e. a SpatialGridDataFrame object)
fDrawfigure extracts either a random point or a probability weighted random point falling within a specified home range. The random point, the observed locations, the specified ud vertice or the utilisation distribution can be plotted on a user-defined background.
This function converts a nearest neighbour (codenb) object into a format which can be accepted by the SOCPROG 2.4 program (Whitehead 2009). The output table generated by this function creates a series of 'expected' associations according to what would be expected if individuals within a population distributed themselves at random within their home range. Random points may be determined within a home range polygon or according to a probability surface within an individual's utilisation distribution. The output table is a dataframe with a group identifier (i.e. those individuals occurring within a defined distance threshold) and each line corresponding to a different individual. Note, the running time of this function is positively related to the number of associations in the system. Running time can be decreased by reducing the numbers of individuals contained in the study or by decreasing the association distance range in variables d1 and d2.
This function plots the output data spatially, allowing the user to visualise the random locations generated for individuals and the resulting associations defined by group memberships. This map can be plotted with or without individual ID labels.
This function combines the functions fRanXY, dnearneigh and fAssoctable, to enable the user to extract repeated expected associations over multiple permutations. Unlike fAssoctable, this function operates in a single step where the user is only required to input the home range of interest, the association distance threshold Gprox and those individuals to be used in the analysis. The user is offered the flexibility to include all individuals in the analysis, or a series of possible combinations of individuals based on mean group size and probability of occurance. The output table generated by this function creates a series of 'expected' associations according to what would be expected if individuals within a population distributed themselves at random within their home range. The output table is a data frame with a permutation number, a group identifier (i.e. those individuals occurring within a defined distance threshold) and the individual id. Each line in a single permutation corresponds to a unique individual. Note, the running time of this finction is positively related to the number of associations in the system. Running time can be decreased by reducing the number of permutations iPerm, limiting numbers of individuals contained in the study iID or by decreasing the maximum association distance threshold Gprox.
Digiroo2 is an R package developed by researchers at the University of Queensland to investigate association patterns and social structure in wild animal populations. Proximity between individuals is generally considered to be an appropriate proxy for associations and pairwise association indices are the most widely used technique for analysing animal social structure. However, little attention is given to identify how patterns of spatial overlap affect these association patterns (Carter et al. 2009). For example, do individuals associate randomly with others with whom they share home ranges, or do some individuals go out of their way to associate with or avoid particular individuals? This program uses home range methodologies to build a null model of space use with individuals exhibiting a 'random' social structure. Random points may be generated within a specified home range contour or according to the Utilization Distribution (UD). Expected associations of individuals are extracted based on probability of occurrence and the proximity between home range weighted random points. Association matrices can be generated from multiple permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to create 'expected' pairwise half-weight association indices (HWIs). These may be compared with the 'observed' HWIs from field observations to reveal whether pairs of animals associate more (= attraction) or less (= avoidance) than expected by chance.
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