Cost‐effective assessment of forest restoration projects: merging advanced techniques with comprehensive analysis

Forest restoration projects affect the entire ecosystem; therefore, they require comprehensive environmental monitoring to ensure their success and counteract unexpected challenges. In this context, complementary monitoring indicators should be selected based on available budget and time, along with advanced analytical techniques to enable a cost‐effective, more comprehensive evaluation of ecosystem outcomes. Here, we describe how measuring tree community composition, aboveground biomass of trees, soil nutrients, and soil microbes using high‐throughput sequencing can provide a broader understanding of the restoration trajectory, especially when environmental variables are considered together using machine learning algorithms. As a successful example of this approach, we report the results of a case study conducted in naturally regenerating Acacia mangium plantations. Ultimately, this research serves as a robust baseline and validation of the efficiency of advanced techniques for studying complementary indicators of forest restoration across forest biomes and geographic regions.