Background/Question/Methods Community ecologists have proposed that dispersal mechanisms, environmental factors, and biotic interactions determine how species are filtered into local communities from regional species pools based on their traits. Nearly two decades ago, this conceptual framework was adapted for restoration ecology to help explain why some species fail to disperse, establish, and persist in restored communities and to identify management solutions. By manipulating species composition and environmental conditions, restoration experiments can provide an opportunity to test ecological theory and address important questions in population and community ecology, such as what processes drive community assembly. Here, I review recent work that tests the trait-based community assembly framework in restored grassland and highlight how an increased focus on traits could move the fields of community and restoration ecology forward. Results/Conclusions Dispersal limits restoration in many grassland communities while others are simultaneously limited by dispersal, environmental factors, and biotic interactions. Recent work in restored grassland has demonstrated potentially important interactions among filters and that the relative impact of filters can vary over time and space. While several studies found that species differentially respond to filter manipulations, surprisingly few explicitly considered how species traits influence establishment and persistence in response to management strategies. A better understanding of how traits interact with community assembly filters will allow for site-specific management recommendations resulting in restored communities that are resilient to filter modifications including climate change, invasion by non-native species, and disturbance regimes. A greater emphasis on mechanistic traits, such as seed and root traits, will enhance predictions of germination, seed bank persistence, and establishment under specific abiotic and biotic constraints. An example of how trait-filter interactions can be used to generate site-specific planting recommendations based on optimal trait values is provided for two grassland systems.
