Use of precise spatial data for describing spatial patterns and plant interactions in a diverse Great Basin shrub community

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Community-structuring processes continue to be of great interest to plant ecologists, and plant spatial patterns have been linked to processes including disturbance, dispersal, environmental heterogeneity, and plant interactions. Under the assumption that the analysis of the spatial structure of plant communities can help to elucidate the type and importance of the predominant community-structuring processes, many studies have analyzed point pattern data on various plant species. A variety of methods have been devised to acquire point pattern data for individual plants, however, the classic tradeoff between the speed of acquisition and the precision of spatial data has meant that large and precise datasets on plant locations are difficult to obtain. The primary goal of this study was to develop a GPS-based methodology for the rapid collection of precise spatial data on plant locations in a semi-arid shrubland in the Great Basin, USA. The secondary goal was to demonstrate a potential application of this approach by using recently developed univariate and bivariate spatial statistics to test for aggregation within the shrub community, as observed in other semi-arid shrublands. We efficiently mapped 2,358 individuals of five shrub species with a spatial error of ≤0.02 m, and found strong statistical evidence of fine-scale aggregation (1) independent of species, (2) within species, and (3) between two species pairs. Our approach is useful for rapidly collecting precise point pattern data in plant communities, and has other applications related to population modeling, GIS analysis, and conservation.