Exploring the relationship between forest type, deforestation drivers and socio-economic wellbeing in the Global South.

Forests are a crucial pillar of household economies in the Global South, with uncultivated natural resources contributing an average of 22% of household income in impoverished regions. Yet, this resource base is increasingly jeopardized by tree cover loss, frequently coinciding with high poverty, forcing communities to expend greater effort to collect resources and thereby increasing livelihood vulnerability. At the same time, poverty acts as a driver of deforestation, creating a destructive feedback loop between socio-economic hardship and environmental loss. While poverty alleviation can be a key conservation strategy, the specific ecological contexts and forest management approaches that may most effectively support local economies remain poorly understood.  Here, we empirically investigate if and how distinct forest types (primary , naturally regenerating, or planted forest), deforestation rates, and deforestation drivers are associated with the wealth patterns of local communities. To do so, we combine global remote-sensing derived datasets on key forest characteristics and land use with granular settlement-level wealth data, specifically the Relative Wealth Index (RWI). First, we extracted for Madagascar, Brazil and Indonesia, forest type and deforestation driver data within a 2.5 km buffer around rural settlements, trying to model where people might travel to collect natural resources, and then averaged the data at the second administrative level. The relationship between these characteristics and the RWI was then investigated using Generalized Additive Models (GAMs) to capture complex, non-linear associations of forest characteristics and RWI for each country. Preliminary results from this proof-of-concept analysis reveal context-specific patterns.  High coverage of primary forest or naturally regenerating forest is consistently associated with lower RWI across all three countries, confirming that potential conservation target areas are often poverty hotspots. Furthermore, while a higher deforestation rate correlates with lower RWI , the drivers of this loss vary significantly by regional wealth : in the three investigated countries, deforestation due to infrastructure building is more prevalent in higher RWI areas, whereas shifting cultivation is a primary driver in lower RWI areas. Analysis of these patterns aims to generate policy insights for targeted forest strategies, promoting a dual-benefit approach for conservation and well-being.