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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 B0.02 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.

Traditional biodiversity metrics operate at the level of a plant community but do not capture spatial variation in diversity from a ‘plant's‐eye view’ of a community. Recently‐developed statistics consider the spatial patterns of plants as well as the number and distribution of species in local plant neighborhoods to quantitatively assess multispecies spatial patterns from a ‘plant's‐eye view’. We used one such statistic, the individual species area relationship (ISAR), to assess spatial patterns of species diversity in a Great Basin (USA) semi‐arid shrubland through an analysis of a spatial dataset on shrub species and locations. In conjunction with appropriate null models, the ISAR blends species area relationships with second‐order spatial statistics to measure the expected species richness in local neighborhoods of variable size around the individuals of a focal species within a community. We found that, contrary to a previous analysis using more traditional methods, the community was well‐mixed with a typical shrub surrounded on average by 4.9 shrub neighbors of 2.1 species at a neighborhood scale of 1.0 m. We also found statistically significant fine‐scale variation in diversity patterns, such that neighborhoods of two species were more diverse than expected by a heterogeneous Poisson null model that accounted for larger‐scale habitat heterogeneity. However, this effect was caused by intraspecific aggregation of these species and was not due to positive interspecific association. Contrary to previous findings in other semi‐arid shrublands, our analysis suggests that the spatial pattern of the shrub community was not significantly structured by interspecific facilitation. This result supports growing evidence for balanced species patterns of adult plants in multispecies communities. Our approach may be used in other communities to describe complex multispecies spatial patterns, quantify species‐specific associations with diversity patterns, and to generate hypotheses regarding relationships between patterns and community‐structuring processes.

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