Description
Effective Forest Landscape Restoration (FLR) requires decision-making that integrates ecological evidence with the knowledge, priorities, and socioeconomic realities of local communities. In this study, geospatial analysis and community-derived socioeconomic evidence were integrated within the Restoration Opportunities Assessment Methodology (ROAM) to support restoration planning in the Okomu forest landscape of southern Nigeria.
Spatial analyses identified priority areas for restoration based on landscape characteristics, while household surveys across seven forest-dependent communities assessed livelihoods, household well-being, perceived health, gender roles, participation in decision-making, social inclusion, and forest resource use. Statistical analyses revealed significant differences in livelihood composition among communities for both female (χ² = 59.85, df = 24, p < 0.001) and male respondents (χ² = 46.55, df = 30, p = 0.028). Agriculture was the dominant livelihood among men, whereas women were more engaged in trading and small-scale enterprises in several communities. Communities also differed in livelihood diversification, well-being, and patterns of forest resource use, highlighting considerable social heterogeneity across the landscape.
Our findings demonstrate that restoration opportunities cannot be prioritized solely on ecological suitability. Livelihood dependence, gendered resource use, and local governance structures influence both the feasibility and long-term success of restoration interventions. Integrating community-derived socioeconomic evidence with spatial restoration priorities provides a more comprehensive basis for designing locally appropriate, socially inclusive, and ecologically effective restoration strategies.
This study shows how integrating spatial data with local knowledge strengthens evidence-based restoration planning, providing a transferable framework for equitable Forest Landscape Restoration and landscape governance.
| Topic | Topic 1: : Methods for integrating social and ecological knowledge |
|---|