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Reducing Emissions from Deforestation and Forest Degradation (REDD+) is considered a promising strategy to slow down deforestation rates, promote sustainable forest use, and support rural livelihoods under the umbrella of climate change mitigation. However, so far there is only little field-based knowledge on how REDD+ can go along with subsistence-based production systems and livelihoods of forest-dependent communities. We addressed this research gap by analyzing the income generation of three widespread ethnic groups (Colonists, Shuar, Kichwa) in the buffer zone of the Yasuni National Park in Ecuador to better understand their livelihoods and possible engagement in REDD+. We selected two communities of each ethnic group (close-to and far-from markets) and used household surveys to (a) calculate household incomes, (b) assess the degree of forest-dependency, and (c) discuss how REDD+ schemes can be designed along with traditional subsistence-based production systems. We found that the studied indigenous communities have a higher degree of forest-dependency and higher environmental income compared to Colonists. However, our assumption that close-to-market communities have a lower degree of forest dependency and higher cash income due to better market access and labor opportunities applies only to the Colonists and Shuar, but must be rejected for the Kichwas who gain income from timber sale. Despite these differences, all communities receive high off-farm revenues from unskilled labor provided by oil-companies and external aid. Therefore, dependency on agriculture and forestry is temporarily reduced. Under these circumstances, REDD+ provides only weak financial incentives so that the willingness to participate in REDD+ is low.

Since the 1980s, mechanized soybean production in Bolivia has caused extensive deforestation in the northeast of Santa Cruz de la Sierra in the eastern Bolivian lowlands. We analyze the spatial and temporal dynamics of deforestation due to mechanized agriculture with spatially explicit logistic regression models. Deforestation patterns are derived from the classification of Landsat imagery by Killeen et al. (2007) and include five time steps (1976, 1986, 1992, 2001 and 2005). We associate deforestation with geophysical and socioeconomic determinants. Our model controls for spatial autocorrelation and temporal dependencies, and we assess the robustness of the results for several model formulations. The expansion of mechanized agriculture is concentrated in areas with favorable environmental conditions, good market access and close proximity to prior deforestation. While overall dynamics remained relatively stable over time, the expansion of mechanized agriculture between 2001 and 2005 became more tolerant to excessive rainfall and less dependent on fertile soils. This mirrors the increasing penetration of mechanized agriculture into humid and less fertile Amazonian rainforests in the northern portion of the study area. The map of deforestation probability substantiates these patterns and shows the highest propensities for future deforestation in the north. Our study demonstrates the value of spatial regression models to better understand the development of deforestation dynamics over a 30-year time span, and contributes to the formulation of policies that aim to reduce deforestation. Yet the results are sensitive to hidden correlations between independent variables, and we therefore advocate a careful evaluation of regression results for different model formulations.