Journal
ECOLOGY LETTERS
Volume 24, Issue 3, Pages 498-508Publisher
WILEY
DOI: 10.1111/ele.13667
Keywords
Climate change; drought; ecosystem modelling; palaeoecology; stability; vulnerability
Categories
Funding
- National Science Foundation MacroSystems Biology [DEB-1241891, DEB-1241868, DEB-1241874, DEB-1241851]
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This study examines ecosystem sensitivity to centennial-scale hydroclimate variability and finds that the spatial patterns in ecosystem responses are strongly governed by sensitivity rather than exposure. Model-data comparisons suggest that interactions among biodiversity, demography, and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability. Integrating ecosystem models with observations beyond the instrumental record can improve understanding and forecasting of forest sensitivity to climate variability in a changing world.
Forecasts of future forest change are governed by ecosystem sensitivity to climate change, but ecosystem model projections are under-constrained by data at multidecadal and longer timescales. Here, we quantify ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was higher in models than observations and highest in simpler models. Model-data comparisons suggest that interactions among biodiversity, demography and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. Integrating ecosystem models with observations from timescales extending beyond the instrumental record can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.
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