Percolation threshold analyses can detect community assembly processes in simulated and natural tree communities
- Studies of spatial point patterns (SPPs) are often used to examine the role that density-dependence (DD) and environmental filtering (EF) play in community assembly and species coexistence in forest communities. However, SPP analyses often struggle to distinguish the opposing effects that DD and EF may have on the distribution of tree species.
- We tested percolation threshold analysis on simulated tree communities as a method to distinguish the importance of thinning from DD EF on SPPs. We then compared the performance of percolation threshold analysis results and a Gibbs point process model in detecting environmental associations as well as clustering patterns or overdispersion. Finally, we applied percolation threshold analysis and the Gibbs point process model to observed SPPs of 12 dominant tree species in a Puerto Rican forest to detect evidence of DD and EF.
- Percolation threshold analysis using simulated SPPs detected a decrease in clustering due to DD and an increase in clustering from EF. In contrast, the Gibbs point process model clearly detected the effects of EF but only identified DD thinning in two of the four types of simulated SPPs. Percolation threshold analysis on the 12 observed tree species' SPPs found that the SPPs for two species were consistent with thinning from DD processes only, four species had SPPs consistent with EF only and SPP for five reflected a combination of both processes. Gibbs models of observed SPPs of living trees detected significant environmental associations for 11 species and clustering consistent with DD processes for seven species.
- Percolation threshold analysis is a robust method for detecting community assembly processes in simulated SPPs. By applying percolation threshold analysis to natural communities, we found that tree SPPs were consistent with thinning from both DD and EF. Percolation threshold analysis was better suited to detect DD thinning than Gibbs models for clustered simulated communities. Percolation threshold analysis improves our understanding of forest community assembly processes by quantifying the relative importance of DD and EF in forest communities.
Journal:Ecology and Evolution