Prioritizing sites for ecological restoration based on ecosystem services.
Restoration ecology that maximizes ecosystem services (ES) requires planning at large spatial scales, which are often the most meaningful for ecosystem functioning and ES supply. As economic resources to undertake ecological restoration at large scales are scarce, prioritizing sites to enhance multiple ES supply is critical. We present the Relative Aggregated Value of Ecosystem Services (RAVES) index, to prioritize sites for ecological restoration based on the assessment of multiple ES. We tested the spatial heterogeneity of ES to identify the relevant scale to managing ES and to apply the RAVES index using a local case study. We also used the RAVES index to compare three alternative restoration scenarios to enhance ES based on the availability of socio-economic resources. The highest RAVES values were found in areas with natural vegetation and in gorges with riparian forests. The lowest values were found in crop fields, steep slopes and river stretches without riparian forest. The multiscale spatial analysis indicated that most ES showed significant heterogeneity at multiple spatial scales, especially at broad (20-30 km) and very broad (40-50 km) scales. For spatial scales smaller than 2 km, only biological control showed significant heterogeneity. The optimal socio-economic conditions to enhance ES supply were met when both private and public land, together with economic funds, were available to implement ecological restoration. As most areas with low RAVES were in private lands, even with limited funds restoration of private lands would result in a large increase in RAVES. Synthesis and applications. The Relative Aggregated Value of Ecosystem Services (RAVES) index is a practical tool to hierarchically prioritize sites for ecological restoration across large spatial scales. The RAVES index integrates both ecological information and societal values by weighting ecosystem services (ES) via a multicriteria analysis and can be used in scenario analysis to identify optimal management scenarios. We highlight the importance of analysing the spatial heterogeneity of ES to identify the most relevant scale to applying the RAVES index and to managing ES via ecological restoration.