Critical transitions in the Amazon forest system

Nature. 2024 Feb;626(7999):555-564. doi: 10.1038/s41586-023-06970-0. Epub 2024 Feb 14.

Abstract

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

MeSH terms

  • Droughts / statistics & numerical data
  • Environmental Restoration and Remediation / trends
  • Feedback
  • Forests*
  • Global Warming* / prevention & control
  • Global Warming* / statistics & numerical data
  • Trees* / growth & development
  • Uncertainty
  • Wildfires / statistics & numerical data