Applying a Functional Trait-based Approach for Ecological Succession Monitoring and Species Management in Areas under Active Restoration in a Tropical Forest.

Monitoring restoration areas is crucial for understanding how ecological succession changes over time and whether the trajectories of planted communities are following the desired path of recovery. A functional trait-based approach coupling the functional trajectory analysis (FTA) with species abundance modelling may have a significant potential application in restoration assessment. In the present study, we surveyed a 10-year-old restoration tree community in the Brazilian Atlantic Forest, planted in rows (2 × 2 m spacing) and clusters (13 individuals planted 0.3 m apart from each other), called active restoration strategy, and compared to natural regeneration without planting, or a passive restoration strategy. Using a multidimensional analysis of six key functional traits (leaf area, specific leaf area, leaf dry matter content, seed dry mass, wood density, and potential height), we employed FTA to assess the ecological successional trajectories of active row and cluster plantations and compared them to a reference ecosystem (REF) and the passive strategy (baseline). Our results indicated over ten years of succession, natural regeneration in plantations resembled communities in passive strategy, showing more abundance of early successional species (more acquisitive). We found natural regeneration in plantations led the trajectory of the planted communities away from the functional dimension of REF. Regeneration in row plantations exhibited higher potential height and leaf area than in clusters, increasing the distance of trajectory from REF. Based on the abundance of species traits in plantations, we were able to detect which species should be removed (thinning) or replanted (enrichment) to potentially redirect undesirable trajectories. We offer a robust functional approach for monitoring and to guide restoration projects toward desired ecological outcomes.