SEI Contact:

Javier Godar

Mobile: +46 73 707 8546

Time-frame: 2013–2016

SES AMAZON-New spatially explicit approaches to understand Amazonian social-ecological systems and actor-specific deforestation responsibility: implications for REDD policies

The on-going environmental degradation of the Amazon threatens key ecosystem services of local and global importance, and is driven by the socio-economic interactions of a large array of actors. Thus, policies targeting a sound rural development for the Amazon need to be informed about the respective social-environmental trade-offs of the diversity of actors living in the area, which is a strong shortcoming of previous and current policy decisions.

Against the backdrop of agrarian reform, climate change and ongoing deforestation, the goal of SES-AMAZON is to build a framework to help regional policymakers make optimal choices between different rural development options. In particular we aim to generate new insights into how social ecological systems (SES) function in the Amazon region, trying to analyze how to better adapt UN-REDD mechanisms to the observed actions of stakeholders and the dynamics of SES. The project has a strong quantitative and methodological focus, applying a SES perspective that integrates natural and social sciences to quantify and compare actor-specific trade-offs in the Brazilian Amazon (5,5 mill.Km2).

We will combine in a GIS environment new graph-based spatial approaches with recently available socio-economic databases at the census scale, using a complex networks perspective. Regionally adapted landscape configuration metrics obtained from remote-sensing maps will be used as indicators of ecosystem services provision. Spatially-explicit modeling will provide insights on the functioning of Amazonian SES under different governance settings. Although the SES analysis will be performed at the Brazilian Amazon scale, four REDD projects will be selected as case studies to analyze the degree of adaptation of REDD schemes to Amazonian SES dynamics.