10.5061/DRYAD.MPG4F4R0S
Charney, Noah
0000-0002-7625-1581
University of Maine
Bastille-Rousseau, Guillaume
Southern Illinois University Carbondale
Yackulic, Charles
United States Geological Survey
Blake, Stephen
0000-0002-2220-1589
Saint Louis University
Gibbs, James
State University of New York
Forecasting NDVI in the Galapagos
Dryad
dataset
2021
FOS: Earth and related environmental sciences
Climate change
Climate
Climate change impacts
vegetation productivity
Galapagos
ecosystem response
downscaling
Forecasting
2021-10-05T00:00:00Z
2021-10-05T00:00:00Z
en
461550721 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Forecasting ecosystem response to climate change is critical for guiding
policy-making but challenging due to: complicated relationships between
microclimates and regional climates; species’ responses that are driven by
extremes rather than averages; the multifaceted nature of species’
interactions; and the lack of historical analogs to future climates. Given
these challenges, even model systems such as the Galapagos Islands, a
world-famous biodiversity hotspot and World Heritage Site, lack clear
forecasts for future environmental change. Here, we developed a novel
non-parametric method for simulating the ecosystem futures based on
observed vegetation productivity (NDVI) during analogous weather observed
historically. Using satellite images taken from the past to piece together
a simulated future, we projected that productivity of terrestrial
vegetation of the Galapagos will increase over the next century by
approximately one standard deviation archipelago-wide, with increases
largest during the wet season (January to June), and in the arid
zones. This greening may impact a variety of ecological and evolutionary
processes, species of conservation concern, and agricultural
practices. Our straightforward approach can be applied to many other
regions, particularly those with rapid ecosystem responses to stochastic
inter-annual climatic fluctuations that provide appropriate climate
analogs for forecasting.