A method to detect subcommunities from multivariate spatial associations

Summary 1. Species are seldom distributed at random across a community, but instead show spatial structure that is determined by environmental gradients and/or biotic interactions. Analysis of the spatial co-associations of species may therefore reveal information on the processes that helped to shape those patterns. 2. We propose a multivariate approach that uses the spatial co-associations between all pairs of species to find subcommunities of species whose distribution in the study area is positively correlated. Our method, which begins with the patterns of individuals, is particularly well-suited for communities with large numbers of species and gives rare species an equal weight. We propose a method to quantify a maximum number of subcommunities that are significantly more correlated than expected under a null model of species independence. 3. Using data on the distribution of tree and shrub species from a 50 ha forest plot on Barro Colorado Island (BCI), Panama, we show that our method can be used to construct biologically meaningful subcommunities that are linked to the spatial structure of the plant community. As an example, we construct spatial maps from the subcommunities that closely follow habitats based on environmental gradients (such as slope) as well as different biotic conditions (such as canopy gaps). 4. We discuss extensions and adaptations to our method that might be appropriate for other types of spatially referenced data and for other ecological communities. We make suggestions for other ways to interpret the subcommunities using phylogenetic relationships, biological traits and environmental variables as covariates and note that subcommunities that are hard to interpret may suggest groups of species and/or regions of the landscape that merit further attention.