ACC News Brief
Ecosystems Global
ENSO may reshape vegetation resilience across much of the planet
What happened
A peer-reviewed Nature Communications study used satellite leaf-area data from 1981 through 2018 to examine how El Nino-Southern Oscillation variability aligns with a statistical indicator of vegetation resilience. The authors estimate a significant ENSO influence across 53 percent of vegetated land and project that the share affected through local climate anomalies could expand by 7 to 10 percentage points by 2100. The resilience measure is based on lag-one autocorrelation, so it is a warning indicator from statistical analysis and modeling, not a direct observation of ecosystem collapse or a proven tipping point.
Why it matters
If ENSO reorganizes where ecosystems are most sensitive to drought and temperature shocks, seasonal forecasts could help guide fire preparation, restoration, water planning, and ecological monitoring. The result also argues for regional evidence rather than a single global story about ecosystem resilience.
What to watch
- Tests using field observations and multiple resilience indicators, because autocorrelation can be affected by noise, trends, and data processing.
- How remote-sensing, sea-surface-temperature, phenology, and CMIP6 biases change the estimated hotspots.
- Whether operational early-warning systems can use ENSO forecasts without presenting probabilistic ecosystem risk as certainty.
Sources & evidence
- ENSO amplifies global vegetation resilience variability in a changing climatePeer-reviewed, open-access Nature Communications article displayed as published December 4, 2025; DOI: 10.1038/s41467-025-66987-z. Nature lists January 8, 2026 as the version-of-record date, which should remain visible in provenance.
- Persistent DOI record for the ENSO and vegetation-resilience studyStable DOI record for the Huazhong Agricultural University-led study. The paper combines historical satellite analysis with CMIP6 projections and discusses uncertainty in its resilience proxy and modeled inputs.
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