Beijer Fellows

Jean-Baptiste Jouffray | PhD-Candidate

Jean-Baptiste Jouffray is a joint PhD-candidate of the Beijer Institute of Ecological Economics, Global Economic Dynamics and Biosphere at the Royal Swedish Academy of Sciences, and Stockholm Resilience Centre, Stockholm University. He has a background in natural science with an undergraduate degree in Biology of Organisms, Populations and Ecosystems from University Paul-Sabatier (France) and a MSc in Ecology from Stockholm University. In his current research, Jean-Baptiste focuses on marine ecosystems, looking at cross-scale interactions between drivers of social-ecological systems.

Jean-Baptiste  Jouffray


Phone +33 632 326 714
Address: The Beijer Institute of Ecological Economics The Royal Swedish Academy of Science Box 50005 SE-104 05 Stockholm, Sweden


Jean-Baptiste wrote his MSc thesis in 2013 at Stockholm Resilience Centre, investigating multiple coral reef regimes and their drivers across the Hawaiian archipelago. He then joined the Global Economic Dynamics and Biosphere program at the Royal Swedish Academy of Sciences as a research analyst for one year, during which he added social and economic layers to his ecological knowledge by mostly focusing on transnational corporations in the seafood production industry and their links with marine ecosystem globally. Today, as a PhD-candidate in Sustainability Science, his primary interest lies in exploring the intertwined relationship between humans and the marine ecosystem through a transdisciplinary approach that uses social-ecological systems as a filter for both the analytical and the interpretative processes. By studying interactions between proximate and distal drivers across scales, the ambition is to provide empirical novel approaches and analytical methods of general value for understanding global dynamics and social-ecological systems. So far, he relied on a combination of statistical methods including among others Principal Component Analysis, clustering analysis, change-point analysis, and Boosted Regression Trees.