egetation dynamics involves processes operating at widely different spatial and temporal scales, from stomatal opening and closing (minutes to days, at the leaf level) to biome shifts (decades to centuries, across entire continents). Tremendous research efforts have been devoted to understanding and predicting how plant processes and functional traits of individuals combine to determine the structure, function and dynamics of vegetation on larger scales. To integrate process understanding from different disciplines, dynamic vegetation models (DVMs) have been developed that combine elements from plant biogeography, biogeochemistry, plant physiology, forest ecology and micrometeorology. The best-known DVMs, dynamic global vegetation models (DGVMs), have found a wide field of application, including assessments of land-atmosphere carbon, water and trace gas exchanges; water resources; impacts of environmental change on plants and ecosystems; land management; and feedbacks from vegetation changes to regional and global climates 1,2. DVMs have also been applied on local scales for testing of ecological hypotheses and to answer practical questions in forest management and agriculture. All DVMs are based on the assumption of universally valid processes, which, in principle, enable them to make predictions under conditions outside the range of observations used for model development.

Trade can allow countries to overcome local or regional losses (shocks) to their food supply, but reliance on international food trade also exposes countries to risks from external perturbations. Countries that are nutritionally or economically dependent on international trade of a commodity may be adversely affected by such shocks. While exposure to shocks has been studied in financial markets, communication networks, and some infrastructure systems, it has received less attention in food-trade networks. Here, we develop a forward shock-propagation model to quantify how trade flows are redistributed under a range of shock scenarios and assess the food-security outcomes by comparing changes in national fish supplies to indices of each country's nutritional fish dependency. Shock propagation and distribution among regions are modeled on a network of historical bilateral seafood trade data from UN Comtrade using 205 reporting territories grouped into 18 regions. In our model exposure to shocks increases with total imports and the number of import partners. We find that Central and West Africa are the most vulnerable to shocks, with their vulnerability increasing when a willingness-to-pay proxy is included. These findings suggest that countries can reduce their overall vulnerability to shocks by reducing reliance on imports and diversifying food sources. As international seafood trade grows, identifying these types of potential risks and vulnerabilities is important to build a more resilient food system.

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