干旱区生态环境知识资源中心

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.