1. Attempts to infer underlying ecological process from observed patterns in ecology have been widespread, but have generally relied on first-order (non-spatial) community characteristics such as the species abundance distribution (SAD). This measure has become an important test of several theories of species coexistence, but has proved unsuccessful in distinguishing between them.
2. Spatially explicit data are increasingly available for a range of ecological communities, and analysis methods for these data are well developed. However, relatively little work has investigated the potential of these data for similar inference about mechanisms of coexistence.
3. In this study, we systematically investigate the spatial and non-spatial signals of simulated ecological processes. We include neutral, niche, lottery, Janzen-Connell and heteromyopia models, deriving and comparing first-and second-order measures for the patterns they generate.
4. We find that the SAD is unable to distinguish reliably between underlying models, with random variation in its shape concealing any systematic differences.
5. A new second-order summary measure of spatial structure derived in this paper, in contrast, proves highly sensitive to the type of ecological interaction being modelled, and is robust to random variation.
6. Synthesis. A simple summary measure of the spatial structure of plant communities is presented and found to be a more powerful indicator of underlying process in simulated data than a widely used first-order measure, the SAD. The potential for answering important ecological questions using spatial statistics, particularly concerning mechanisms of coexistence in diverse communities, appears to be great.
- coexistence mechanisms
- determinants of plant community diversity and structure
- pair correlation function
- spatial point pattern
- species abundance distribution
- species abundance distributions
- tropical forests
- neutral theory
- relative abundance
- point patterns