A generalizable monitoring model to implement policies to promote forest restoration – A case study in São Paulo - Brazil

Examples of large-scale restoration programs to recover ecosystem services are now common in many countries, and governments are assuming ambitious forest restoration targets. Given the increasing investment of time, effort, and money in restoration, there is an urgent need to develop monitoring programs to assess restoration effectiveness. Some countries are already conducting monitoring programs, but the effectiveness of the restoration programs remains mostly unknown. Restoration evaluation often entails significant difficulties, such as the lack of harmonized monitoring data and imprecise information available about project goals and implementation. With the intent of contributing to the development of effective and accountable restoration projects, the objective of our work is to create a conceptual model that provides the building blocks of a planning and monitoring system to support forest restoration programs. The aim is to develop a conceptual model that represents forest restoration monitoring processes that effectively attain and measure the desirable outcomes. The São Paulo Forest Restoration Program is the case study that provides variables and processes to illustrate the development of the conceptual model. This paper presents the conceptual model, emphasizing generalizable principles that extend its applicability to similar monitoring programs. Based on action learning principles and recommendations from a comprehensive literature review, the resulting Forest Management Decisions Support System (FMDSS) embeds adaptive management strategies and the existence of an auto-updatable knowledge base. The result is a conceptual model that can be generalizable and applicable beyond the realms of the FMDSS. The restoration of degraded areas in a case with >40,000 rural properties serves as the case study. Although the planning and the monitoring of the restoration programs differ, the generalizable principles used to develop the conceptual model presented in this paper result in continuous intelligent monitoring processes that transform the systems so that they are adaptable to apparently different situations. Additionally, conceptual models that integrate adaptive planning and monitoring processes, supported by an auto-updatable knowledge base, mitigate the risk of failures, mainly when the comprehensive gathering of well-established references for the initial knowledge base has been conducted well at the outset.