The interacting influences of competition, composition and diversity determine successional community change

1. Community change is one of the few constants in nature, and the balance of mechanisms influencing this change is central for understanding the structure and functioning of communities and ecosystems. Newly established communities undergo succession and can change in diversity and composition as local environmental change, interspecific interactions and immigration play out over time. Understanding the influence of initial conditions and priority effects (long-term consequences of the initial community composition and species identity) on community change is critically important for both evaluating ecological theory and predicting restoration outcomes. 2. Here I evaluate how initial experimental conditions in 2012, like initial sown species richness, phylogenetic diversity and early biomass production, along with priority effects caused by the identity of sown species, influence subsequent herbaceous plant community composition and the number of colonizing species after 9 years of uninterrupted natural colonization. 3. I found that sown phylogenetic diversity (measured as mean pairwise distance) indirectly affected the number of colonizing species by increasing biomass production early on and plots with more biomass in 2012 were colonized by fewer species. Individual species influenced the number of colonizing species with taller species reducing the number of colonists and some shorter species subject to a greater number of colonists. 4. Synthesis. Taken together, these results indicate that initial composition influences the number of colonizing species via community-wide competition. These findings suggest that restoration outcomes can be greatly influenced by decisions about sown species composition and early management practices.

Automated summary

Community change is a constant in nature, and understanding the influence of initial conditions and priority effects is important for evaluating ecological theory and predicting restoration outcomes.