Prioritizing areas for ecological restoration: A participatory approach based on cost-effectiveness

1. Landscape-scale prioritization models are powerful decision-making tools in ecological restoration. Yet, they often fail to integrate multi-stakeholder perspectives and socio-ecological criteria. 2. We designed a new methodology to identify high-priority areas for landscape-scale restoration. This participatory cost-effectiveness analysis model is based on execution and maintenance costs and the potential increase in the supply of multiple ecosystem services. 3. We tested the model in a 181,000 ha heavily anthropized semi-arid landscape in southeastern Spain. Restoring the whole area would cost 221 million EUR and enhance the supply of ecosystem services by 39%. The cost-effectiveness of restoring pine forest and abandoned and irrigated crops were higher than restoring other Landscape Units. Restoring the least degraded sites was more cost-effective than the most degraded areas or randomly selecting sites, even when potential recovery was incomplete. 4. Synthesis and applications. The cost-effectiveness of restoration actions depends on the type of ecosystem and degradation state. Visualizing the outcomes of alternative restoration scenarios needs participatory prioritization maps based on financial costs and the potential supply of ecosystem services. We propose a participatory prioritization protocol that is flexible and adaptable and can help government agencies, environmental managers, investors, consultancies and NGOs' plan restoration actions at the landscape scale and optimize the effectiveness of restoration programs.This research has been financially supported by the Ministry of Science, Education and Universities, the Ministry of Economy, Industry, and Competitiveness of the Spanish Government, and European Regional Development Funds (FEDER; projects TERECOVA CGL2014-52714-C2-1-R and COSTERA, RTI2018-095954-B-I00), and Conselleria d'Innovació, Universitats, Ciència i Societat Digital, Generalitat Valenciana (Project R2D, CIPROM/2021/001)