Network analysis as a tool for quantifying the dynamics of metacoupled systems: An example using global soybean trade

Abstract

The metacoupling framework provides grounds for characterizing interactions within and between coupled human and natural systems, yet few studies quantify the nuances of these systems. Network analysis is a powerful and flexible tool that has been used to quantify social, economic, and ecological systems. Our objective was to evaluate the utility of network analysis for quantifying metacoupled systems by assessing global soybean trade among 217 countries from 1986 to 2013. We identified and quantified sending and receiving systems, subnetworks and flow pathways, changes over time and across scales, feedbacks, and associations between trade and tropical deforestation. Although a total of 165 distinct cliques were identified within the network, a few key players were disproportionately influential in the 2872 partnerships, including Brazil (37.5%), China (48.6%), and the USA (72.3%). Total network density increased five-fold over the study period with an increasingly smaller set of countries heavily engaged in trade, posing sustainability and food security concerns. We found evidence of a positive feedback where countries with established trade partnerships were more likely to expand trade relationships over the study period. Trade patterns were not explained by regional or continental geography, highlighting limitations of neighborhood analyses commonly used in ecology. We also found evidence of a link between soybean trade and tropical deforestation; in pantropical countries participating in soybean trade, cumulative soybean exports for the period 2000–2012 were strongly associated with remotely sensed estimates of forest loss by country (Rsq = 0.35, p < 0.0001). We demonstrated that network analyses can be used to quantitatively assess relationships between metacoupled social-ecological systems. Increased data access and platforms for integrating diverse data sources using multidisciplinary tools will be key to pushing the boundaries of quantitative metacoupled systems research.