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Transient social-ecological dynamics reveal signals of decoupling in a highly disturbed Anthropocene landscape.

Qi LinKe ZhangCharline Giguet-CovexFabien ArnaudSuzanne McGowanLudovic GiellyEric CapoShixin HuangGentile Francesco FicetolaJi ShenJohn A DearingMichael E Meadows
Published in: Proceedings of the National Academy of Sciences of the United States of America (2024)
Understanding the transient dynamics of interlinked social-ecological systems (SES) is imperative for assessing sustainability in the Anthropocene. However, how to identify critical transitions in real-world SES remains a formidable challenge. In this study, we present an evolutionary framework to characterize these dynamics over an extended historical timeline. Our approach leverages multidecadal rates of change in socioeconomic data, paleoenvironmental, and cutting-edge sedimentary ancient DNA records from China's Yangtze River Delta, one of the most densely populated and intensively modified landscapes on Earth. Our analysis reveals two significant social-ecological transitions characterized by contrasting interactions and feedback spanning several centuries. Initially, the regional SES exhibited a loosely connected and ecologically sustainable regime. Nevertheless, starting in the 1950s, an increasingly interconnected regime emerged, ultimately resulting in the crossing of tipping points and an unprecedented acceleration in soil erosion, water eutrophication, and ecosystem degradation. Remarkably, the second transition occurring around the 2000s, featured a notable decoupling of socioeconomic development from ecoenvironmental degradation. This decoupling phenomenon signifies a more desirable reconfiguration of the regional SES, furnishing essential insights not only for the Yangtze River Basin but also for regions worldwide grappling with similar sustainability challenges. Our extensive multidecadal empirical investigation underscores the value of coevolutionary approaches in understanding and addressing social-ecological system dynamics.
Keyphrases
  • climate change
  • human health
  • healthcare
  • mental health
  • risk assessment
  • genome wide
  • gene expression
  • machine learning
  • brain injury
  • subarachnoid hemorrhage
  • life cycle
  • plant growth