A probabilistic approach to estimating species pools from large compositional matrices

Species pools are increasingly recognized as important controls of local plant community structure and diversity. While existing approaches to estimate their content and size either rely on phytosociological expert knowledge or on simple response models across environmental gradients, the proposed application of phytosociological smoothing according to Beals exploits the full information of plant co‐occurrence patterns statistically. Where numerous representative compositional data are available, the new method yields robust estimates of the potential of sites to harbour plant species.

To test the new method, a large phytosociological databank covering the forested regions of Oregon (US) was subsampled randomly and evenly across strata defined by geographic regions and elevation belts. The resulting matrix of species presence/absence in 874 plots was smoothed by calculating Beals' index of sociological favourability, which estimates the probability of encountering each species at each site from the actual plot composition and the pattern of species co‐occurrence in the matrix. In a second step, the resulting lists of sociologically probable species were intersected with complete species lists for each of 14 geographical subregions. Species pools were compared to observed species composition and richness. Species pool size exhibited much clearer spatial trends than plot richness and could be modelled much better as a function of climatic factors. In this framework the goal of modelling species pools is not to test a hypothesis, but to bridge the gap between manageable scales of empirical observation and the spatio‐temporal hierarchy of diversity patterns.