Finding the perfect mix: An applied model that integrates multiple ecosystem functions when designing restoration programs

Abstract

We present an applied model that helps restoration practitioners select an ideal mix of species to plant in order to meet their restoration objectives. The model generates virtual plant communities designed to optimize the delivery of multiple ecosystem functions. We used an optimization approach to find the most cost-effective combinations of species to plant to optimize the delivery of four ecosystem functions: rapid establishment of vegetation cover, soil building, biological soil health and resistance to invasion. We used trait-function relationships to characterize species' effects on ecosystem functions. This model accounts for key operational constraints selected by the user, including budget, the number of species to plant, and which functions to consider. The user can also decide whether or not to maximize the functional diversity of the species mix to increase its resilience to global environmental change. To demonstrate the practicality of this approach, we derived optimal species mixtures for the restoration of forests damaged by Cu-Ni smelters in the City of Greater Sudbury (Ontario, Canada). The species mixtures generated by the model varied according to which functions and operational constraints were selected. Results show that the species mixtures that were the most effective at delivering multiple functions were also cost-effective, but were less functionally diverse. This tool provides restoration practitioners with cost-effective restoration strategies for managing the recovery of multi-faceted socio-economic and environmental values in disturbed landscapes.