• Integrate, synthesize, and share scientific, government, and citizen knowledge across study sites;
  • Promote knowledge exchange about adaptive management and use of river ecosystem modeling between the US and Brazil;
  • Advance learning, theoretical and methodological approaches in inter- and trans-disciplinary research on social-ecological systems;
  • Provide insights and recommendations for planning, management, monitoring and decision-making in existing and future hydroelectric dam construction projects.

Framework and Mission:

The Amazon Dams Network (ADN) is an interdisciplinary and international group of researchers, students, and various stakeholders collaboratively studying the interconnected set of social-ecological effects of hydroelectric dam construction and operation across the Amazon basin.

The ADN mission is to build capacity for the advancement of inter- and transdisciplinary research on the social-ecological impacts of hydroelectric dam construction in the Amazon. The network aims to synthesize and share lessons learned from hydroelectric dam  implementation in the Amazon and the United States focusing on an adaptive management approach within the complex social-ecological systems (SES) theory.

The geographical focus includes the Tocantins, Madeira and Xingu River watersheds in the Amazon, and the Colorado River watershed in the US, specifically focusing on the Glen Canyon Dam Adaptive Management Program (GCDAMP).

The ADN initial group of participants includes researchers from diverse fields, representatives from governmental and non-governmental institutions, and underrepresented social groups from the US, Brazil, Bolivia and Peru.

In Brazil, ADN leaders are the Amazonian Universities Federal University of Rondônia (UNIR) and Federal University of Tocantins (UFT).

In the US, the ADN is hosted in the Tropical Conservation and Development Program in the Center for Latin American Studies at the University of Florida. It also partners with the USGS Glen Canyon Dam Adaptive Management Program (GCDAMP).

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